Email: Reasoning Models Can Be Effective Without Thinking

Subject:
Reasoning Models Can Be Effective Without Thinking
Received:
4/22/2025, 6:02:29 PM
From:
"The AI Timeline" <weekly@mail.bycloud.ai>
To:
desert.glitter.81@getmynews.app
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&lt;/style&gt;&lt;/head&gt;&lt;body class="a" style="margin:0px auto;padding:0px;word-wrap:normal;word-spacing:normal;background-color:#dedede;"&gt;&lt;div role="article" aria-roledescription="email" aria-label="email_name" lang="en" style="font-size:1rem"&gt;&lt;div style="display:none;max-height:0px;overflow:hidden;"&gt; Plus more about BitNet b1.58 2B4T Technical Report and ReTool: Reinforcement Learning for Strategic Tool Use in LLMs &#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204; &lt;/div&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" align="center" cellpadding="0" class="gg"&gt;&lt;tr&gt;&lt;td align="center" valign="top"&gt;&lt;table role="none" width="670" border="0" cellspacing="0" cellpadding="0" class="aa" style="width:670px;table-layout:fixed;"&gt;&lt;tr&gt;&lt;td class="bodyWrapper" align="center" valign="top" style="padding:7px 7px 7px 7px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="border-width:0px 0px 0px 0px;border-style: solid; border-color: #2a2a2a;border-radius:10px 10px 0px 0px;background-color:#ffffff;" class="c"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr id="header"&gt;&lt;td style="padding:28px 28px 0 28px;"&gt;&lt;div style="padding-top:0px;padding-right:0px;padding-bottom:20px;padding-left:0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td class="f" align="right" valign="top"&gt;&lt;p&gt; April 22, 2025 &nbsp; | &nbsp; &lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3Ews41rqXvjZRdxHJoRvyky9GrRFjWMUouVBhnL3rUfvXKh549otN7gDdrRkl8qAtor3wVX6Z1wPXg6LmQ-j_HBVLw2T8udyZDDkdAEbTffx5uHgYfW-dlrawrBaMgUJnLlMuzC_bq40nmFCCsiEnpgvuZRB5L2atQFBHDv5CjYcMJVrDb9f6Eu4TdDLWft3_jGWI98W_NmHEx0fQyWheu0-XId-kQsZtTZpwd21Wb4S8YBQ8idxVyZq73r8nB3EZMkkb45qhnMM2cm4qfSL6-u_fJHnAe-gWEWBUDoxEyEf7T9xatBXRjEaPShSMCqU8gnDPRrV2DwiIG93uk3fnoaKHG3xWFM0Xq0CaeMHJlJgQ50CDEYV54-uWUrxFO-QflhxCh1S0Q1Al23DQl23MB6OKaPu1d6u_xP9Rd3TM0CD-VEnUkjM244ZaRxujEQksBJC2h84NzQ1cQiNBlvZt-DEDUAK_t_teen3tCTgl7caqEw6xu3JQbIDeOoOhSGKsvpgCpnC_wt8qYMRE5MvC69sydTqSYHTbe81CTO92Fbqh24P_l3xMoimJDsQxt_ZeJBR6WOBvvVAcYOsjjbAHE9zATGKX3dnDDlhjE7_PmYX5SB80K5VzCQpNwAfKRLUTrc8uDT-4tCr6pq6_sYuXu6P/4fv/q9Q2jAbZQJe8t242fSCrMQ/h0/h001.qNORNLe4rpS-fT4o78LH6CgZfGYry_Uh0TLXWXDWUTo"&gt;&lt;span class="translation_missing" title="translation missing: en.templates.posts.email.v3.header.read_online"&gt;Read Online&lt;/span&gt;&lt;/a&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="center" valign="top" style="padding:15px 0;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="center" valign="top"&gt;&lt;h1 style="text-align:left;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-weight:Bold;font-size:32px;color:#2A2A2A;padding:2px 0;line-height:38px;"&gt; Reasoning Models Can Be Effective Without Thinking &lt;/h1&gt;&lt;p style="text-align:left;font-family:'Helvetica',Arial,sans-serif;font-weight:normal;font-size:20px;color:#3E3E3E;padding:5px 0;line-height:24px;"&gt; Plus more about BitNet b1.58 2B4T Technical Report and ReTool: Reinforcement Learning for Strategic Tool Use in LLMs &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="height:0px;width:0px;"&gt;&lt;div style="height:1px;" data-open-tracking="true"&gt; &lt;img src="https://elink4f7.mail.bycloud.ai/ss/o/u001.3wmUuY8gEWd4_869a_eXcg/4fv/q9Q2jAbZQJe8t242fSCrMQ/ho.gif" alt="" width="1" height="1" border="0" style="height:1px !important;width:1px !important;border-width:0 !important;margin-top:0 !important;margin-bottom:0 !important;margin-right:0 !important;margin-left:0 !important;padding-top:0 !important;padding-bottom:0 !important;padding-right:0 !important;padding-left:0 !important;"/&gt; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr id="content-blocks"&gt;&lt;td class="email-card-body" align="center" valign="top" style="padding-bottom:28px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td id="nov-18-th-nov-24-th-33-latest-ai-re" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h6 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;i&gt;Apr 14th ~ Apr 20th&lt;/i&gt;&lt;br&gt;&lt;i&gt;#52 Latest AI Research Explained Simply&lt;/i&gt;&lt;/h6&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="industry-news-in-1-line" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;"&gt;🗞️ Industry News in 1 Line&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ol start="1" style="list-style-type:decimal;margin:0px 0px;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt;♥ 10k&lt;/span&gt;&lt;/span&gt; &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DIqyAo9xTeoWriogq2VlWeUmi9WmFR4pnC4wMSHAHOF6_6w25mM725nDWiEX2adwkAr695IC2Q-LLRR4sl_Myk1wXs_gOmUdAtVk8fdMSJYQdCI-0Oxz8m8FEgBr0ntgppM05l_GsH73vFrp0cKaxw/4fv/q9Q2jAbZQJe8t242fSCrMQ/h1/h001.dBmyGjhkhXHlJ4EwhYmbQJtKTm8Nftb9mEBk8LCZ8SQ" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;OpenAI announced o4-mini and o3&lt;/span&gt;&lt;/a&gt;, their smartest and most capable models yet. With o4-mini becoming the new state-of-the-art, and o3-(high) topping the &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtod7Z0dEFnd1F2hiwXXdgX9dF6U1lYSxqDKl9qxsJzfuLSiEbDTu1c2ICCyzrOf_Dv1Befs1mMq2oY-abNskm7k3BgLwgkoy3S-nUelu2nd4N/4fv/q9Q2jAbZQJe8t242fSCrMQ/h2/h001.XXT8gpsGXNFvqPxe_fFgbCeX5w5nfb7xwqJRjkRLJb4" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;aider LLM leaderboard&lt;/span&gt;&lt;/a&gt;. Both are multi-modal models capable of vision understanding, with their API price starting at $10 in/$40 out for o3, and $1.1 in/$4.4 out for o4-mini &lt;/p&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/7da6f023-6a93-4c75-91bf-78ceafedc596/Screenshot_2025-04-22_123746.png?t=1745339902" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:600px;"&gt;&lt;p&gt;via Artificial Analysis&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/b811114a-49d5-460c-9b26-19b9e8371ab7/Screenshot_2025-04-22_124614.png?t=1745340400" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:600px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtod7Z0dEFnd1F2hiwXXdgX9dF6U1lYSxqDKl9qxsJzfuLC2v7K0Fa0_9XkcbZA4ltDa36NnlFPXvcnQcUMC1nnXvCPKAAtwjBUUMxdC9nUbxT/4fv/q9Q2jAbZQJe8t242fSCrMQ/h3/h001.Gus-IzWVnnG2LLtouCS0wmJzP3bB6QmT7gSVZFO0xZ8" target="_blank" rel="noopener noreferrer nofollow" style="text-decoration:underline;text-decoration-color:#000000;"&gt;&lt;p&gt;current Aider LLM leaderboards standing&lt;/p&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/li&gt;&lt;/ol&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="transparent" style="background-color:transparent;border-color:#2C81E5;border-style:solid;border-width:5px;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;"&gt;&lt;span style=""&gt;bycloud’s new project: search AI papers semantically!&lt;/span&gt;&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1r-Oo1v2APbJapoJieKk3Wpe7ldTezUu5UQjr3YxkjDr64dknbQx1-38CKAMc8wQVG5Tk8oQAyiI7e9mg3sBDvoe5v43TFe36B13zOTMDEQ/4fv/q9Q2jAbZQJe8t242fSCrMQ/h4/h001.r5Lk3-jtmxe02_Xdb5NO1dM4dVtoTfF5910RPO1l_TA" rel="noopener noreferrer nofollow" style="text-decoration:none;" target="_blank"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/0f46c7c1-9068-4af6-882f-1c91dd0370d7/Screenshot_2025-04-22_122639.png?t=1745339251" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;Hey guys! It must have been a long wait for some of y’all, and I am very excited to share with you my latest project that I just shipped, called:&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="center" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:center;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;"&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1r-Oo1v2APbJapoJieKk3Wpe7ldTezUu5UQjr3YxkjBTNAZ-93a5i8IJfW4-u9rJZxkf6XD6ekQp-u8OhENEm59imNEQOPS73uHpJ7HDfy4/4fv/q9Q2jAbZQJe8t242fSCrMQ/h5/h001.ij4aeLye9CikYDoCnXcy_BE083H9oc1usVxtSa33oUg" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;findmypapers.ai&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="center" style="padding:0px 28px;text-align:center;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;A semantic search engine&lt;/span&gt;&lt;span style=""&gt;&lt;b&gt; &lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt;for 300k+ AI research papers!&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;&lt;b&gt;Outcompete Deep Research apps&lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt; like Grok, OpenAI, Perplexity, and Gemini at finding relevant papers. Check out our &lt;/span&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1muhFWIqieRYpaJ-FbWSCWCRiFtPjyCLdYnvOYF_NjbdiUG6yBzqs-QmgqsDemTXEfGUA21vS3OZ4E6M6xfiTGp7YYx3gaLkjTkhlNaFTopQiPJIYVkvC50pCBupRUoAe4IKjUNECKfEztm0hA9gkw/4fv/q9Q2jAbZQJe8t242fSCrMQ/h6/h001.3Paso8HB4LErXryhNrDWXa0c5aWtIxKdOv3WuH49kIs" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;demo on X&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;span style=""&gt;.&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;Specifically, there are &lt;/span&gt;&lt;span style=""&gt;&lt;b&gt;~300,000 &lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt;AI/ML research papers currently indexed in my engine, that’s about&lt;/span&gt;&lt;span style=""&gt;&lt;b&gt; 1.19TB worth of PDFs&lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt; as a knowledge base. &lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;By next month, we are planning to increase this by 4x, indexing the entire Arxiv.org. &lt;/span&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.YPehixmk-kG3xNpo0v-G8f3o7VcdtHB83ZVhpubVzjwrUmEWhbWzbjVEwJ1zgX-qob7dMPx1NV363Mro6M4WOcZzLExGT0039jk9liA0ELw/4fv/q9Q2jAbZQJe8t242fSCrMQ/h7/h001.smfHMiUVFMp12K3Chtok3FUreNKG9wtH-3g5FPGGbeQ" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Findmypapers.ai&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;span style=""&gt; is now available as a &lt;/span&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.zNfxTwpJFmrsCuJJphGRkKSrCVph9-fOYkcjx4VfJRwtQQsKrZC8pi-PiKai2fq4lAto9WepTJo69aQJ1T73b1BYaJHeCrLz1cWpFYfpKjdJ071BkzwRo9IrCS5YAIxy/4fv/q9Q2jAbZQJe8t242fSCrMQ/h8/h001.ifW143_lrLas2JUxqah4zOexsV6p2gUU3Qo3f1KD-pQ" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Patreon benefit&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;span style=""&gt; or you can access it directly on the website.&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;&lt;b&gt;&lt;i&gt;But why ANOTHER search engine&lt;/i&gt;&lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt;&lt;b&gt;?&lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt; So there are currently 2 problems for each existing solution:&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ol start="1" style="list-style-type:decimal;margin:0px 0px;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;Generative AI models trained with papers is built for serving hallucination&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;Deep Research Agents is good but wasting compute by browsing 80% SEO optimized slop&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1r-Oo1v2APbJapoJieKk3Y7-_bQjp1fXYJa9abKZXRUgyOs1NPSA74uaogeIHbgopbVpZ_VynKBPt72DltmApNNJhGKptQzw6LEjgr_5wTg/4fv/q9Q2jAbZQJe8t242fSCrMQ/h9/h001.BejwtECh1sSEPHELB07gZP7cm0oFeTpfWqYpPwE9Ja8" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;findmypapers.ai&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;span style=""&gt; addresses both of these problems, and takes the best of both worlds.&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;I believe that surveying research shouldn’t be that hard. You can be as specific and technical about your search query, and it’ll not give you made up unuseful bs.&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1muhFWIqieRYpaJ-FbWSCWCRiFtPjyCLdYnvOYF_NjbdiUG6yBzqs-QmgqsDemTXEfGUA21vS3OZ4E6M6xfiTD05fi_VevfdgUQq8MCIS1JZSj19TNidtWwVKO_qUVQBDPn-tIMPpha_LChHos34Hw/4fv/q9Q2jAbZQJe8t242fSCrMQ/h10/h001.rp6wMt3CpUcJRnuils-uDxkTF5-SuJP-J4CxwxiKYbE" rel="noopener noreferrer nofollow" style="text-decoration:none;" target="_blank"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/ce5a5c8f-0719-46af-b0f6-94a4bd3a1cf2/Screenshot_2025-04-22_122657.png?t=1745339260" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;&lt;span style=""&gt;snippet of the output results&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;Before u try:&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ul style="font-weight:normal;list-style-type:disc;margin-bottom:12px !important;margin-top:12px !important;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;Search time is long (est. 1~3min depending on search range)&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;Limited to AI research papers, but will be fixed soon once we have money to upgrade our storage&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style=""&gt;Broad/wide search is really REALLY useful if you need big compilation of information like my own use cases&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;To Celebrate our launch, us code &lt;/span&gt;&lt;span style=""&gt;&lt;b&gt;BETAN50 &lt;/b&gt;&lt;/span&gt;&lt;span style=""&gt;for 50% off for the next 2 months! (50 limited redeems). You can follow our official &lt;/span&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.1muhFWIqieRYpaJ-FbWSCYfUZkPCVN-dWoLNWR_ZMb_doXnIaIcMVbwXdkB4cr2YfIXSLmfL5_kJtoxgXDNoI3BY9hlplzWMX9BDXz8A5pkI0L4iVuxL_TKxqAqtcaeZ/4fv/q9Q2jAbZQJe8t242fSCrMQ/h11/h001.yGKGgn651TuYsf59-TolifLNvqsPdG_Xb3AnICD1RB4" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;X account&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;span style=""&gt; or join &lt;/span&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpWPwAndoLzTm9asQYWRGsHurBY7u08OKEJWmmW9BgfmCCytIDpcDyMus-wNgIKcNzGVVqWeRIMxLMWcsEP5ialNUYt1fn7lWi6QSuqsByBk2/4fv/q9Q2jAbZQJe8t242fSCrMQ/h12/h001.zSKIONUfH4Jlfha_jJRsMH93rrx8skgbBDi8XnRL41A" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Discord&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;span style=""&gt; for any updates. &lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j29rweNtSHTMPdCBNsuu15hSzQcOxkOpREY7CmsUqazs5MMa1uyTWQcHA44VJKvv5LOrj73ya7S6uuAAMt4LiYwQ-PzqQ4QspN0c-91wcDG-u/4fv/q9Q2jAbZQJe8t242fSCrMQ/h13/h001.JxhSRrq3snI9tFS1sDKcxMjiXuu_AgvL0nddhCP8kc8" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"&gt; Check Out FindMyPapers &lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;&lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.tLfGW26lAwaS9gFg17HSoGymQ3NNPtd5dE5MV_8UgjIDFPVXngz8pvQBldSW42yhUe_Qiq6DgEPMEBuPL9yfRpXelTiuu2kS8pLFvsoem_XoZoy_n13sTKUhZIbl0VH6/4fv/q9Q2jAbZQJe8t242fSCrMQ/h14/h001.ncdhE2apR_O0F0QDV2Xt-9X7xxMG11Uk65UVOBU6kkw" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Advertise with The AI Timeline! &lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="re-tool-reinforcement-learning-for-" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;"&gt;ReTool: Reinforcement Learning for Strategic Tool Use in LLMs&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;&lt;i&gt;Feng et al. [ByteDance Seed]&lt;/i&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥ 400 &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; LLM RL &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="how-reinforcement-learning-teaches-" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;How Reinforcement Learning Teaches LLMs to Think with Tools&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; LLMs have remarkable reasoning abilities and they can perform advanced actions from solving logic puzzles to generating step-by-step explanations. However, they still struggle with mathematical tasks like solving Olympiad-level equations or geometric proofs. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; This is primarily because pure text-based reasoning struggles with exact calculations and symbolic manipulation. For instance, solving a combinatorics problem might require them to calculate permutations, a task where a single arithmetic error derails the entire solution. Code interpreters (CIs) offer a workaround by enabling precise, executable steps, but existing methods to combine LLMs with tools rely on imitation learning.  &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; This paper introduces &lt;span style="font-weight:700;"&gt;&lt;b&gt;ReTool&lt;/b&gt;&lt;/span&gt;, a framework that reimagines how models &lt;span style=""&gt;&lt;i&gt;learn&lt;/i&gt;&lt;/span&gt; to integrate computational tools like code interpreters into their reasoning. ReTool tackles this problem by treating tool use as a &lt;span style=""&gt;&lt;i&gt;skill to be learned&lt;/i&gt;&lt;/span&gt;, not just mimicked, using outcome-driven RL. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/2ca4d2dd-83f7-482b-96f4-cd8c0109d0f9/code-rl.png?t=1745334083" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;AIME 2024 & 2025 scores of ReTool and text-based RL baseline on the Qwen2.5-32B-Instruct model. The x-axis represents the training steps.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="how-re-tool-trains-models-to-think-" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;How ReTool Trains Models to &quot;Think with Code&quot;&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; ReTool operates in two phases. First, it builds a foundation through &lt;span style="font-weight:700;"&gt;&lt;b&gt;cold-start training&lt;/b&gt;&lt;/span&gt;: synthetic data teaches the model basic tool invocation by replacing manual calculations in existing reasoning traces with code snippets and their execution results. This dataset, refined through format and answer verification, primes the model to recognize &lt;span style=""&gt;&lt;i&gt;when&lt;/i&gt;&lt;/span&gt; and &lt;span style=""&gt;&lt;i&gt;how&lt;/i&gt;&lt;/span&gt; to insert code blocks during problem-solving. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; In the second phase, we will use a &lt;span style="font-weight:700;"&gt;&lt;b&gt;tool-integrated RL &lt;/b&gt;&lt;/span&gt;that interacts with a code sandbox in real time (unlike standard RL, which generates text-only reasoning chains). As the model writes a reasoning step, it can pause to generate a code block (marked by &lt;span style="color:rgb(24, 128, 56);"&gt;&lt;code&gt;&lt;/span&gt; tags), execute it, and receive feedback (success results or errors) within &lt;span style="color:rgb(24, 128, 56);"&gt;&lt;interpreter&gt;&lt;/span&gt; tags. This dynamic loop allows the model to adjust its strategy based on whether the code worked, and whether the final answer was correct. For example, if a generated Python snippet throws a &lt;span style="color:rgb(24, 128, 56);"&gt;NameError&lt;/span&gt;, the model might revise its code in the next step, learning to define missing variables. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/90a19fee-dd95-4f72-bdf2-93fa5d581b4a/image.png?t=1745334129" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; To streamline training, ReTool also uses asynchronous code sandboxes for parallel execution and masks interpreter feedback during loss calculation. This sparse signal drives the model to explore strategies that &lt;span style=""&gt;&lt;i&gt;reliably&lt;/i&gt;&lt;/span&gt; reach solutions, prioritizing not just correctness but efficiency. Over time, the model discovers patterns like early tool invocation (to validate assumptions quickly) or chaining code snippets for multi-step proofs. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/3d3bcd64-9ddd-447c-a8a7-c404c1917f2f/image.png?t=1745334192" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="results-shorter-reasoning-smarter-c" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;Results: Shorter Reasoning, Smarter Code, Better Accuracy&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; ReTool shows  impressive performance on the challenging AIME Olympiad benchmarks. A 32B model trained with ReTool achieved &lt;span style="font-weight:700;"&gt;&lt;b&gt;67% accuracy&lt;/b&gt;&lt;/span&gt; on AIME 2024 in just 400 training steps, outperforming text-only RL baselines (40% accuracy, 1,080 steps). With a stronger backbone (DeepSeek-R1), accuracy rose to &lt;span style="font-weight:700;"&gt;&lt;b&gt;72.5%&lt;/b&gt;&lt;/span&gt;, surpassing OpenAI’s o1-preview by 27.9%. Three findings stand out: &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ol start="1" style="list-style-type:decimal;margin:0px 0px;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="font-weight:700;"&gt;&lt;b&gt;Efficiency Gains&lt;/b&gt;&lt;/span&gt;: Responses became 40% shorter post-training, as models replaced verbose calculations with concise code. &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="font-weight:700;"&gt;&lt;b&gt;Strategic Tool Use&lt;/b&gt;&lt;/span&gt;: Code invocation shifted earlier in reasoning chains, and the variety of code purposes (e.g., verification, enumeration) expanded. &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="font-weight:700;"&gt;&lt;b&gt;Emergent Self-Correction&lt;/b&gt;&lt;/span&gt;: Models began debugging their own code. In one case, after a &lt;span style="color:rgb(24, 128, 56);"&gt;NameError&lt;/span&gt;, a model added missing function definitions and reran the code, a behavior never explicitly taught. &lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/079be3e2-5726-473a-aa03-b26c35bda7c3/image.png?t=1745334158" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; However, it still has a few limitations. The reliance on rule-based answer verification assumes problems have unambiguous solutions, which may not hold in open-ended domains. Additionally, while ReTool reduces hallucination, &lt;span style="font-weight:700;"&gt;&lt;b&gt;errors in code logic&lt;/b&gt;&lt;/span&gt; (e.g., off-by-one bugs) can still propagate if not caught by the interpreter. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j28yjf9KIXZdsXoh1WlHvvKnifmDudL33vDWLTY9Dtn6Xeu_3YwhNBTBVYiHy2JVDffWIIBij6NzQ569k6vPjeM2bn4chlNnXFWd8RDnarZl0/4fv/q9Q2jAbZQJe8t242fSCrMQ/h15/h001.Ls0TwUuhFUc8BpnufJ5EJd7u_ZG0dwwM4pB7Dx0azqk" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"&gt; Read Full Paper &lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="bit-net-b-158-2-b-4-t-technical-rep" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;"&gt;BitNet b1.58 2B4T Technical Report&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;&lt;i&gt;Ma et al. [Microsoft Research]&lt;/i&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥ 518 &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; LLM Compression &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="how-1-bit-ll-ms-are-redefining-effi" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;How 1-Bit LLMs Are Redefining Efficient AI&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; The race to build leaner, faster language models often feels like squeezing a mountain into a shoebox. Advanced LLMs excel at complex tasks but they require hefty computational resources which makes them impractical for edge devices or real-time applications.  &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; But to everyone’s surprise &lt;span style="font-weight:700;"&gt;&lt;b&gt;BitNet b1.58 2B4T&lt;/b&gt;&lt;/span&gt;, a 2-billion-parameter model that challenges the status quo by operating almost entirely with 1-bit weights, has proven that bit based LLMs might just scale very well.  &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/11d7d95b-6bba-46f8-8e84-41673d560d5c/image.png?t=1745334790" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;BitNet b1.58 2B4T advances the Pareto frontier&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="the-efficiency-revolution-ternary-w" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;The Efficiency Revolution: Ternary Weights Meet Smarter Training&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; Many LLMs use quantization tricks to save resources, but this model uses a completely new architecture which was trained from scratch to deliver performance at low cost. Instead of compressing weights after training, it uses a new architecture that uses only ternary values (-1, 0, +1) for every layer from day one. This “native” 1-bit design reduces memory use while sidestepping the performance drops seen in retrofitted models. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/78df9bac-a96a-4938-afbb-c1ff07361c1f/image.png?t=1745334837" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; It uses &lt;span style="font-weight:700;"&gt;&lt;b&gt;BitLinear layers&lt;/b&gt;&lt;/span&gt;, which replace standard linear layers in Transformers. These layers quantize weights to ternary values during forward passes using an absolute mean scheme which ensures numerical stability. In the next step, the activations get trimmed to 8-bit integers which further cuts compute costs. To keep training on track, the team borrowed techniques from high-performance LLMs: rotary positional embeddings, ReLU-squared activations for sparsity, and a bias-free design.  &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; For training this mode, the researchers used a two-phase learning rate schedule that started aggressively to achieve1-bit stability, then cooled down to refine high-quality data. They also used weight decay as a temporary guardrail early on before being disabled, letting parameters settle into precise configurations. The dataset contained a mix of 4 trillion tokens of web crawls, educational content, and synthetic math data, with later stages emphasizing curated examples.  &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/cbbe879c-ede9-4987-bf02-0fe61144500f/image.png?t=1745334870" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Comparison of BitNet b1.58 (2B) against Qwen 2.5 1.5B in its original bf16 precision and after INT4 post-training quantization.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="bit-net-b-158-performance-benchmark" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;BitNet b1.58 Performance Benchmarks&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; BitNet b1.58 performs well on benchmarks against similarly sized models like LLaMA 3.2 1B and Gemma-3 1B. It matches or exceeds performance in language understanding (MMLU), reasoning (ARC-Challenge), and math (GSM8K) while using &lt;span style="font-weight:700;"&gt;&lt;b&gt;6x less memory&lt;/b&gt;&lt;/span&gt; (0.4GB vs. 2.6GB). Furthermore, it outperforms INT4-quantized versions of larger models like Qwen2.5-1.5B which proves that native 1-bit training beats post-hoc compression. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/ab354ffe-9304-40b6-b67e-ea82cd415531/image.png?t=1745334928" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Performance comparison of BitNet b1.58 2B4T against other open-weight 1-bit models.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j28yjf9KIXZdsXoh1WlHvvKnHGX5iTA2-YIkhSbtFEbe95-L3cNue3aOJlYywsmWXhXW8xq3KhXe3WcDy5YhdERVBaszvcL4xtV0w9UflYRqt/4fv/q9Q2jAbZQJe8t242fSCrMQ/h16/h001.0qp1_t37Xayyd8a2XTotmFDjwgxHhqhuM40_o6q5x7Y" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"&gt; Read Full Paper &lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""&gt;&lt;tr&gt;&lt;td bgcolor="#222222" style="background-color:#222222;padding:0.0px 0.0px 0.0px 0.0px;"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="reasoning-models-can-be-effective-w" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:Bold;padding:0px 28px;text-align:left;"&gt;&lt;h2 style="color:#2A2A2A;font-weight:Bold;"&gt;Reasoning Models Can Be Effective Without Thinking&lt;/h2&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style=""&gt;&lt;i&gt;Ma et al. [University of California, Allen Institute for AI]&lt;/i&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥ 385 &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; LLM Reasoning &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; bycloud’s pick &lt;/span&gt;&lt;/span&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="can-language-models-skip-the-thinki" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;Can Language Models Skip the &quot;Thinking&quot; Step?&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; Modern LLMs often approach complex tasks by generating elaborate, step-by-step reasoning traces. These “chain-of-thought” processes are widely considered essential for solving challenging problems in mathematics, coding, and logic. While effective, this process significantly increases computational costs, latency, and token usage. Researchers have tried optimizing these thinking steps by shortening them or training models to prioritize concise reasoning, but all still assume explicit reasoning is indispensable. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; This paper aims to reduce this computing cost by cutting out the thinking steps entirely and replacing it with a minimal, prefilled placeholder. The researchers are calling this method &lt;span style="font-weight:700;"&gt;&lt;b&gt;NoThinking&lt;/b&gt;&lt;/span&gt;, this method forces the model to skip its usual reflective process and jump directly to generating solutions.  &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/64a2a261-669c-44ee-ad53-7509f1a23456/image.png?t=1745334306" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="how-no-thinking-approach-works" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;How NoThinking Approach Works&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; The NoThinking method uses a simple prompting tweak where instead of letting the model generate a verbose thinking block, the researchers artificially insert a short, empty placeholder (e.g., “&lt;span style=""&gt;&lt;i&gt;Okay, I think I have finished thinking.&lt;/i&gt;&lt;/span&gt;”) and prompts the model to continue from there. This bypasses the model’s default behavior of generating extended self-dialogue and effectively truncates the reasoning process. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/56bd5c85-fefd-422c-88ed-6e54dd94e0fb/image.png?t=1745334437" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; By capping the total tokens available for generation, both traditional “Thinking” and NoThinking methods are forced to prioritize efficiency. In low-budget settings (e.g., 700 tokens), NoThinking outperforms Thinking by wide margins, 51.3% vs. 28.9% accuracy on the AMC 2023 math benchmark. Additionally, NoThinking’s performance scales better as the number of sampled outputs (&lt;span style=""&gt;&lt;i&gt;k&lt;/i&gt;&lt;/span&gt;) increases, suggesting its outputs are more diverse or complementary when aggregated. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; Additionally, the authors paired NoThinking with a &lt;span style="font-weight:700;"&gt;&lt;b&gt;parallel scaling approach.&lt;/b&gt;&lt;/span&gt; In this method, they generated multiple independent solutions in parallel and selected the best one using task-specific verifiers (for theorem proving) or confidence-based rankings (for tasks without verifiers).  &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="results-and-implications-of-no-thin" class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:normal;padding:0px 28px;text-align:left;"&gt;&lt;h3 style="color:#2A2A2A;font-weight:normal;"&gt;&lt;span style="color:rgb(67, 67, 67);"&gt;Results and Implications of NoThinking Approach&lt;/span&gt;&lt;/h3&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; The researchers tested NoThinking Approach across seven reasoning benchmarks, including mathematical problem solving (AIME, AMC), coding (LiveCodeBench), and formal theorem proving (MiniF2F): &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding-bottom:12px;padding-left:50px;padding-right:40px;padding-top:12px;" class="ee"&gt;&lt;div style="margin-left:0px;" class="edm_outlooklist"&gt;&lt;ol start="1" style="list-style-type:decimal;margin:0px 0px;padding:0px 0px 0px 0px;"&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="font-weight:700;"&gt;&lt;b&gt;Token Efficiency&lt;/b&gt;&lt;/span&gt;: NoThinking consistently matches or surpasses Thinking when token usage is controlled. For example, on OlympiadBench (a challenging math dataset), NoThinking achieves 51.3% accuracy with 700 tokens, while Thinking manages only 28.9%. At higher token budgets, Thinking catches up in pass@1 metrics, but NoThinking still dominates in pass@k scenarios as &lt;span style=""&gt;&lt;i&gt;k&lt;/i&gt;&lt;/span&gt; increases. &lt;/p&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="line-height:24px;padding:0px;text-align:left;word-break:break-word;"&gt;&lt;span style="font-weight:700;"&gt;&lt;b&gt;Latency Reduction&lt;/b&gt;&lt;/span&gt;: Parallel scaling with NoThinking reduces inference latency by up to 9x compared to sequential Thinking. On theorem-proving tasks, it achieves similar accuracy with 4x fewer tokens and 7x lower latency. &lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:28px;padding-right:28px;padding-top:20px; " class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:600px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/472f3a5c-0304-47f9-8fa4-ac1a72eeb49c/image.png?t=1745334472" alt="" height="auto" width="600" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" class="t" style="width:600px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Comparison of Best-of-N selection methods (majority voting, confidence+highest, and confidence+voting) on selected experiments.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="left" style="padding:0px 28px;text-align:left;word-break:break-word;"&gt;&lt;p style="line-height:24px;"&gt; However, the approach isn’t universally optimal. For coding tasks like LiveCodeBench, NoThinking lags behind Thinking in pass@1 accuracy, likely because code solutions require precise, verifiable outputs that benefit from iterative refinement. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:14px;padding-left:28px;padding-right:28px;padding-top:14px;text-align:center;width:100%;word-break:break-word;" class="dd"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="center" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle" height="44.75" style="height:44.75px;background-color:#2C81E5;border-color:#DFD150;border-radius:10px 10px 10px 10px;border-style:solid;border-width:0px 0px 0px 0px;color:#FFFFFF;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j28yjf9KIXZdsXoh1WlHvvKn66mLD2S5g9I943y4dql0ovEmx6C1TuPeU6ozGEPDlnktjgh5F1X_PrkWQC8tjikdhxtocVWUW3JtwQ8FQO8hd/4fv/q9Q2jAbZQJe8t242fSCrMQ/h17/h001.y-6FzARf6PfiXcW3muRunP9WmTguHI4G2NMmPLR2M_M" target="_blank" rel="noopener noreferrer nofollow" style="color:#FFFFFF;display:block;font-size:16px;font-size:16px;font-weight:normal;padding:0px 14px;padding:14px 14px 14px 14px;text-decoration:none;"&gt; 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The AI Timeline &lt;/span&gt;&lt;span style="display:block;color:#697882;font-size:14px;"&gt; @TheAITimeline &lt;/span&gt;&lt;/td&gt;&lt;td width="24" align="right" style="vertical-align:text-top;"&gt;&lt;img width="24" height="24" loading="lazy" alt="tw" style="border:0;" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/x_logo.png"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="word-break:break-word;"&gt;&lt;p&gt;🚨This week's top AI/ML research papers:&lt;/p&gt;&lt;p&gt;- BitNet b1.58 2B4T Technical Report &lt;br&gt;- Reasoning Models Can Be Effective Without Thinking &lt;br&gt;- ReTool &lt;br&gt;- Sleep-time Compute &lt;br&gt;- Nemotron-H &lt;br&gt;- Kimina-Prover Preview &lt;br&gt;- CLIMB &lt;br&gt;- Dynamic Cheatsheet &lt;br&gt;- How new data permeates LLM knowledge and how to&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="padding:12px 0 0 0;"&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" style="padding:8px 0 0 0;width:480px;"&gt;&lt;img src="https://pbs.twimg.com/media/GpCD0LxXsAAe7AE.jpg" width="480" height="auto" style="display:block;border:1px solid #E1E8ED;border-radius:5px;width:100%;max-width:480px;height:auto;"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td height="8" style="line-height:1px;font-size:1px;height:8px;"&gt;&nbsp;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="left" valign="top" class="s"&gt;&lt;p&gt;4:34 AM • Apr 21, 2025&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td height="10" style="line-height: 1px; 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