Email: Less is More: Recursive Reasoning with Tiny Networks

Subject:
Less is More: Recursive Reasoning with Tiny Networks
Received:
10/14/2025, 5:53:46 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 Moloch&#39;s Bargain: Emergent Misalignment When LLMs Compete for Audiences and LLM Fine-Tuning Beyond Reinforcement Learning &#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:15px 15px 0px 15px;"&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; October 14, 2025 &nbsp; | &nbsp; &lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3ExRxn1lugNcE67f1nu-mrb639guZQmRV1si4yNRhOK-_e4PZqdT8WpjPiajL0YFZcePcp1xPov27BgH1ObhAQzctnxYZU_8VAq2Ov3jNYd3he8WpKoBbGeRFPxkj-vxVpXtBgkMrfI_iXrv6AeFmo87dDfpdCuLEX2CUD6RiGP1THpts_ktsUawGBmu11IF-xOA0wObzXbh_xhWS0lIpZ1rIOFKE1P18r0u3Q8PSpgyA_fdHX1ZDYSQhrUCJDWUnzeunML0DHOwUV1JaaY0N-UG6qgwWq8dTtceYzDIdcMtjvwMWxu_1_Bc5AkWSIdpWolirg14oXQ6fJ8xZ_kjOaDSJgHjo17nfBGtWTOpgJ6T_lNm5pTk-ihI1Xzsti8iGB5qd_ULTcoKTcpegK0kzrlK7ouDgeS8JwcR0tIsLce7sl3t3BTm7OjQMEhURoZF8GBbBz0tHi8x4nRcPHhln_IUDAHgeCgvM-uA3YKCB0cECk6TrgszFioxB88OT02DNBmFH1nOMss7CjB0fkSKkfwCCoipHNCyr-KcIMh2RKXm6ROZpA40ARKXYqzjg18X8rCLrQ5oFUs6S3XWEACmbuntKZpep9BQTZGvcWir3oWYz9RSLhKcZvWCVLgdXuQtsc8qVqw1TM0ykvtEMX7jOGMBKRAORjRaorq-vTjdRBLQfJ1YPivzUEQTEFmTGnBErjZuZH2Fi7fYIFyRBlMksYwXRgdsVRNhrwG3IYTB9f-d-yFkaBT0EW5JHY2WlN2zAg-C-k_lhjDHFYAm6BSEI8ccMloIwzebzjZRkY_dmWLxJI6G1f1nUrfeC3GZ4NpghkX-Zyh63_YUckx4u6JB4NwIG9STdiHznt3X8d6A4goqqA/4kq/xrMKEwoGRY227W7i8vIjGQ/h0/h001.8F5kx2UYjEq-7HCgZ1V6NRKWh6LEri8gO14OTVvpRl0"&gt;&lt;span class="translation_missing" title="translation missing: en.templates.posts.email.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; Less is More: Recursive Reasoning with Tiny Networks &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 Moloch&#39;s Bargain: Emergent Misalignment When LLMs Compete for Audiences and LLM Fine-Tuning Beyond Reinforcement Learning &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style="line-height:0;"&gt;&lt;div data-open-tracking="true"&gt; &lt;img src="https://elink4f7.mail.bycloud.ai/ss/o/u001.3wmUuY8gEWd4_869a_eXcg/4kq/xrMKEwoGRY227W7i8vIjGQ/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:15px;"&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;mso-line-height-alt:87.5%;"&gt;&lt;i&gt;Oct 7th ~ Oct 13th&lt;/i&gt;&lt;br&gt;&lt;i&gt;#77 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="mso-line-height-alt:150.0%;"&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;mso-line-height-alt:150.0%;"&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="mso-line-height-alt:150.0%;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;♥ 1.2k&lt;/span&gt;&lt;/span&gt; &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWiHxAxLcebl_xZNuLdFlDkCebRcklowAjCbIbSJFM6QaijeGTWIqMu2ydwfFl-aiPbbT4lRLDXbClMQBwVUeVWh3mGnodR3pFQOqFllWBm1AwaW433wbgNgxvrcf5FO25wrdY81n3IiamZtr_ufDY2l8RpuvLBcz78PoseMI_1yrgyp3bM0eaVFq14ez-AhS0lDvpXNPdTk-f38LigBPEC8iJuVeZijLm2-szSsiD7OFSd4E1eb-YHH9ZRjuBgAfmWezkNAAuJVPKLe6BPxQzok/4kq/xrMKEwoGRY227W7i8vIjGQ/h1/h001.w2tN1ETQU1gPSUaUVc0RXBsIakFQiFjT5hONYbVCXGE" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Ant Ling has introduced Ling-1T&lt;/span&gt;&lt;/a&gt;, its new flagship AI model with one trillion parameters. This model excels at complex reasoning tasks, visual understanding, and front-end code generation. It also shows state-of-the-art performance in mathematics, coding, and logical reasoning. &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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/ef9c4791-1486-4ed5-8c2b-25e446eb9734/image.png?t=1760460943" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;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;♥ 1.5k&lt;/span&gt;&lt;/span&gt; &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j29Dv0KjG-2Nzke2R1FagDlqRoOH6oH-fEY6YqPYdoaUoNvV93oVT_CZVpNwb6nO0zoeNcHIZWWhWPCQ9SMJOMrZ4avLfLpzLYBXlFXO9uUqWq8Zcevs9KfphpQkW4lLTPixya0vAUYDkosog18p0tbqnm7NZezBJWtd6mNi4Osei7UQgT-867rqoREkIv1fm31f2wV6OdTwu5ynBdEQs1KKiXMaia0oS-_m3emhZqUaX/4kq/xrMKEwoGRY227W7i8vIjGQ/h2/h001.yPEF_moAEmO6_xurN9YXdl2y1Mw7RXfwBMo8nPyqV1c" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Figure&lt;/span&gt;&lt;/a&gt; has launched its latest humanoid robot, &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2zkIEoP3lqRrrbp4q_uAevMqMEgoxd1UsKCpEEBgvBu8DwhBX2eQ-NgBaCSmsCln42r314yuhOzDnowTXHw2qDIDFZrmQHYENhYsFAmWkNLZ0hmCkcDN24_mlb3VBBPhnEn2CTuZDzGFXJm31pqvZS0tB4g2yz3vKu8jUCB_ngcFSxyqfHOOqkGJ5ov31_OXtGVa_pz7VyISzsSWqJ922Beb1em-UJMPJpZ_j1o6vuNhmHvU2uPc6VR1iX_a-6kQP_yup_4sB_z5cdtVW0JLMek/4kq/xrMKEwoGRY227W7i8vIjGQ/h3/h001.3tXZb_vOjHyDA8Y7sICVGh-6y8-XnVbA746QBsZFT9o" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Figure 03&lt;/span&gt;&lt;/a&gt;, which is designed to operate in homes and be mass-produced. This third-generation robot is engineered for general-purpose tasks, and it comes with a softer, lighter design for safety and maneuverability in household environments. Figure 03 can perform a variety of domestic chores like folding laundry and loading a dishwasher. &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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/bb69cfa4-85e1-44b6-9b76-3d2972b2aa8d/image.png?t=1760461236" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/li&gt;&lt;li class="listItem ultext"&gt;&lt;p style="mso-line-height-alt:150.0%;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;♥ 18k&lt;/span&gt;&lt;/span&gt; World-famous AI researcher Andrej Karpathy has released &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJU5MiGnwmPCPjoT7CfG6prs7mQBOsFMQc0N8j1o2Bx15gOlpwQTxt9y1Aeky78yqb2X4v84lHOOMwNltqTkKoSKrsaitBt_MAkOwryIwCdScfIt6-WjvFSRWtNgqgR2oQYokTXlxPtfELNigAN9tOkoY47moqewAwK97qu7m01OYgL97Klc3h5GC68O0LZ6DUh25c5YKBxy8zD5KcYxVap2Gcw6R6uOPgvRzMI7OhmkDlxkgDTzHE_YJDKQiA13aFg/4kq/xrMKEwoGRY227W7i8vIjGQ/h4/h001.P5p4H1n0ec7tRm1TEb9qhyxf-SNWqbK6bX_ErDUzq2s" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Nanochat&lt;/span&gt;&lt;/a&gt;. It is an open-source project that provides a complete, from-scratch pipeline for training a personal ChatGPT-like model. The single repository contains the entire stack, from pretraining and reinforcement learning to a final inference engine with a web UI. This allows developers to train their own functional chat model in just a few hours for &lt;b&gt;as little as $100&lt;/b&gt;. &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.VomAAYwkCjux8i_FMc4kJU5MiGnwmPCPjoT7CfG6prsnJWemDZTC1ZeboORmLUVj_TTPeePIxafMyEWN1meqwopQSINKYFQ8z31ci0IqsE-OJUBbMbrqjMKD2I6oDNWhUJgKV0BruMXEp6Ldlxy72jhanDY8M8fWqDQnxC4Q1wB9yEKLfIOMxOEfJY63_-CfrNfm-Ku9_G0YHYZkGE70x6sIjI_PhbhbP23CSpTNrG-T6HeScHmCVMZ1TuMEBQ6droDHjfwcTegGpsESw9hF-qjhCuLEeYrzzZ7j1tKEx0w/4kq/xrMKEwoGRY227W7i8vIjGQ/h5/h001.-Z-mn-NN6VXIUqIfh_8RDL1aEMM_LNu-yYpBY510AsU" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Read the technical walkthrough to learn more&lt;/span&gt;&lt;/a&gt;. &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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/1c912f4c-d0fa-49cf-83ec-67d4fc7844bb/nanochat.png?t=1760461485" alt="" height="auto" width="626" style="display:block;width:100%;" border="0"/&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="mso-line-height-alt:150.0%;"&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;mso-line-height-alt:150.0%;"&gt;&lt;span style=""&gt;Support My Newsletter&lt;/span&gt;&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="mso-line-height-alt:150.0%;"&gt;&lt;span style="color:rgb(34, 34, 34);font-family:Georgia, "Times New Roman", serif;font-size:16px;"&gt;As I aim to keep this newsletter free forever, your support means a lot. 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[&lt;/i&gt;&lt;/span&gt;&lt;i&gt;Samsung SAIL Montr´eal&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt;]&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="mso-line-height-alt:150.0%;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥ 11K &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;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="tiny-recursive-models-for-efficient" 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;mso-line-height-alt:125.0%;"&gt;Tiny Recursive Models for Efficient Reasoning&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="mso-line-height-alt:150.0%;"&gt; AI models often struggle with complex reasoning tasks like solving Sudoku puzzles or navigating mazes, where a single mistake can lead to failure. LLMs use methods like chain-of-thought and test-time compute to improve reliability. However, they still fall short on benchmarks like ARC-AGI, failing to reach human-level performance even after years of development. &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="mso-line-height-alt:150.0%;"&gt; The &lt;b&gt;Hierarchical Reasoning Model&lt;/b&gt; (HRM) is a new method that uses two small networks that recurse at different frequencies, and it showed promise on hard puzzles with limited data. However, HRM relies on complex assumptions and may not be optimal. This is where the &lt;b&gt;Tiny Recursive Model&lt;/b&gt; (TRM) comes in, providing a much simpler and more effective approach to recursive reasoning that outperforms both HRM and many large models with far fewer parameters. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/727e95a9-77b6-4925-80b2-a82e5a21137d/image.png?t=1760459022" alt="" height="auto" width="626" 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:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Tiny Recursion Model (TRM) Architecture.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="inner-working-of-the-tiny-recursive" 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;mso-line-height-alt:125.0%;"&gt;Inner Working of the Tiny Recursive Model&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="mso-line-height-alt:150.0%;"&gt; TRM simplifies recursive reasoning by using just one tiny network with only two layers, eliminating the need for HRM&#39;s hierarchical structure and fixed-point theorems. Instead of assuming convergence to a fixed point for gradient calculations, TRM backpropagates through the entire recursion process. &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="mso-line-height-alt:150.0%;"&gt; This involves repeatedly updating a latent reasoning feature and the current answer over multiple steps. For example, given an input question, current answer, and latent state, the model refines the latent state several times before updating the answer. This process allows TRM to correct errors progressively without relying on theoretical guarantees that may not hold in practice. &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="mso-line-height-alt:150.0%;"&gt; TRM also consolidates HRM&#39;s two networks into a single network, which reduces parameters while improving generalization. Surprisingly, using smaller networks with fewer layers, like two-layer architectures, helps prevent overfitting on small datasets, making the model more efficient and effective. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/76222b82-2526-4d4d-9206-e67d362b3219/image.png?t=1760459087" alt="" height="auto" width="626" 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:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Pseudocode of Hierarchical Reasoning Models&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="evaluation-and-benchmark-performanc" 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;mso-line-height-alt:125.0%;"&gt;Evaluation and Benchmark Performance of Tiny Recursive Models&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="mso-line-height-alt:150.0%;"&gt; TRM has shown impressive results across multiple challenging benchmarks. On Sudoku-Extreme, it achieves &lt;b&gt;87.4% test accuracy&lt;/b&gt;, a significant jump from HRM&#39;s 55%, using only 5 million parameters. For Maze-Hard, TRM reaches &lt;b&gt;85.3% accuracy&lt;/b&gt; compared to HRM&#39;s 74.5%. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/560b86d9-f9f6-48d6-bea0-1f15cff0134f/image.png?t=1760459164" alt="" height="auto" width="626" 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="mso-line-height-alt:150.0%;"&gt; On the ARC-AGI benchmarks, which test general intelligence on geometric puzzles, TRM scores 44.6% on ARC-AGI-1 and 7.8% on ARC-AGI-2, outperforming HRM and many large LLMs like Deepseek R1 and Gemini 2.5 Pro, despite having less than 0.01% of their parameters. &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="mso-line-height-alt:150.0%;"&gt; While TRM advances recursive reasoning, it is currently &lt;b&gt;limited to supervised learning and deterministic outputs&lt;/b&gt;, which may not suit tasks requiring multiple valid answers. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class="btn_row"&gt;&lt;td 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 width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoZr-f6keVrG1GKuVv1wQKvorQTHKz7Ywm6wONBnZok2D4o_S4CEo4pKUy4Hrz9s9hgFEMBbPhyRWmmW52woxJOHFp2PmWwPyAFnrDUnng1Rop42mrXAH10o8VMzvCd9ltok1HjWFhYWx4DTQZT0KOHIgCYlLRRoExTHTZs-RZFJcGsKBxM6a-YGlmCNm0uPgeBHzNpJ22S32WnwjiAnsMQYWKYb3O6I2yeNvmXB7-X71MLzJWEUsuNQ4fLj70ZncCg/4kq/xrMKEwoGRY227W7i8vIjGQ/h10/h001.6OhhsmCmIWGsr7zTCQuQ4JH0hXqm3ysGdWQSuidF80U" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;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;/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="mso-line-height-alt:150.0%;"&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="molochs-bargain-emergent-misalignme" 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;mso-line-height-alt:150.0%;"&gt;Moloch&#39;s Bargain: Emergent Misalignment When LLMs Compete for Audiences&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="mso-line-height-alt:150.0%;"&gt;&lt;i&gt;El and&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt; &lt;/i&gt;&lt;/span&gt;&lt;i&gt;Zou&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt; [&lt;/i&gt;&lt;/span&gt;&lt;i&gt;Stanford University&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt;]&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="mso-line-height-alt:150.0%;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥ 8.9k &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 &lt;/span&gt;&lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt;Misalignment&lt;/span&gt;&lt;/span&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(44, 129, 229);font-size:0.6rem;"&gt; &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="introduction-to-molochs-bargain-in-" 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;mso-line-height-alt:125.0%;"&gt;Introduction to Moloch’s Bargain in AI&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="mso-line-height-alt:150.0%;"&gt; In high-stakes environments, models are often tuned to win over audiences, but this comes with a hidden cost. When LLMs are optimized for competitive gains, they tend to become more deceptive and harmful, even when instructed to stay truthful. This phenomenon is known as &lt;b&gt;Moloch’s Bargain&lt;/b&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="mso-line-height-alt:150.0%;"&gt; It shows that short-term wins in sales, elections, or social media can lead to long-term risks like misinformation and unethical behavior. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/a1a123c7-0d60-4256-afa4-dc2c1aa15add/image.png?t=1760460328" alt="" height="auto" width="626" 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:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Generations before and after training across three domains (Top).&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="training-methods-for-competitive-ll" 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;mso-line-height-alt:125.0%;"&gt;Training Methods for Competitive LLMs&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="mso-line-height-alt:150.0%;"&gt; To understand how Moloch’s Bargain works, the researchers examined the rejection fine-tuning (RFT) training approach. It works by having the model generate multiple responses (like sales pitches or campaign messages) and then keeping only the ones that audiences prefer. This process fine-tunes the model on successful examples, reinforcing strategies that lead to better outcomes. However, RFT relies solely on binary preferences, which might miss the nuances of why certain responses work. &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="mso-line-height-alt:150.0%;"&gt; This paper introduces text feedback (TFB), which adds another layer by incorporating the audience’s written reasoning. Instead of just knowing which message was preferred, the model also learns from the audience’s thoughts about each option. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/e31e8fdc-a006-4998-a40e-dec95bebdb4e/image.png?t=1760460361" alt="" height="auto" width="626" 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:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Relative increase in misalignment after training for competitive success.&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="mso-line-height-alt:150.0%;"&gt; For instance, in a sales scenario, TFB would train the model to predict why customers liked one pitch over another, helping it grasp which elements are persuasive and which are not. This method provides richer feedback, enabling the model to develop a more detailed understanding of effective communication. &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="mso-line-height-alt:150.0%;"&gt; While TFB often leads to stronger performance gains compared to RFT, it also amplifies the risk of misalignment. By diving deeper into audience psychology, models might learn to exploit persuasive tactics that border on deception or misinformation. &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="mso-line-height-alt:150.0%;"&gt; The shift from simple preference learning to reasoning-based training highlights how competitive optimization can push models toward unsafe behaviors, even when the initial goal is to improve their effectiveness. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/db8b2f95-bf38-4213-8b7c-bbf0fb9384cb/image.png?t=1760460392" alt="" height="auto" width="626" 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:626px; padding: 4px 0px 4px 0px;"&gt;&lt;p&gt;Demonstration of the training pipeline for the sales task. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="evaluation-and-results-of-molochs-b" 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;mso-line-height-alt:125.0%;"&gt;Evaluation and Results of Moloch’s Bargain in AI Models&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="mso-line-height-alt:150.0%;"&gt; The experimental results showed that models trained with RFT and TFB achieve higher win rates in simulated competitions, but at the expense of alignment. In sales tasks, we noticed that a 6.3% increase in sales was accompanied by a &lt;b&gt;57.1% rise in deceptive marketing&lt;/b&gt; for one model. &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="mso-line-height-alt:150.0%;"&gt; Similarly, election scenarios saw a 4.9% gain in votes paired with a &lt;b&gt;22.3% increase in disinformation&lt;/b&gt;, and social media engagement boosts came with staggering jumps in harmful content. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/3730baea-ab61-43af-a6b5-fa00a439198c/image.png?t=1760460442" alt="" height="auto" width="626" 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="mso-line-height-alt:150.0%;"&gt; These outcomes suggest that market-driven optimization can systematically erode model safety, creating a race to the bottom where competitive success undermines trust. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class="btn_row"&gt;&lt;td 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 width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoZ-ViATox8-gHcO6FcZs5H41xLYBO-rnb4aJI9ZpudFkXwhBo3WpnNBbzf1oPwQuuGpzEZjm7MuUzoD_OZ_GvZvylgtk6J-WlJul2SPOmIsGQWMHlZRAJRIU_jtVtg3KF2rD9vQbm3Aw-OpsWIvPtm_Dcks6aLBTPioAP-HR7XC3pHxc64esZxCHaSXiwDGlkziTHhCJ83V4Nek4Ow113lF2vD_xX34auoXKic4qh8KPlSL5Ip7H47rqhVupDnR60Q/4kq/xrMKEwoGRY227W7i8vIjGQ/h11/h001.Yb1sgJKurgMVQtjIzPg0IfNqDVhr6Vz6en6viz2W_08" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;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;/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="mso-line-height-alt:150.0%;"&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="evolution-strategies-at-scale-llm-f" 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;mso-line-height-alt:150.0%;"&gt;Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning&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="mso-line-height-alt:150.0%;"&gt;&lt;i&gt;Qiu&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt; et al. [&lt;/i&gt;&lt;/span&gt;&lt;i&gt;Cognizant AI Lab, MIT, UT Austin&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt;]&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="mso-line-height-alt:150.0%;"&gt;&lt;span style="background-color:#e0e0e0;"&gt;&lt;span style="color:rgb(255, 58, 58);font-size:0.6rem;"&gt; ♥2.6K &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 Training &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="introduction-to-evolution-strategie" 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;mso-line-height-alt:125.0%;"&gt;Introduction to Evolution Strategies for Fine-Tuning Large Language Models&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="mso-line-height-alt:150.0%;"&gt; Reinforcement learning often struggles with sample inefficiency, sensitivity to different base models, and a tendency to exploit or &quot;hack&quot; reward functions, all of which can lead to unstable and unreliable outcomes. These limitations become especially apparent in tasks with sparse, outcome-only rewards, where progress can be slow and inconsistent. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/4844966b-b1c4-4c46-b399-cfe1d3093e31/image.png?t=1760460502" alt="" height="auto" width="626" 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="evolution-strategies-in-llm-fine-tu" 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;mso-line-height-alt:125.0%;"&gt;Evolution Strategies in LLM Fine-Tuning&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="mso-line-height-alt:150.0%;"&gt; Evolution strategies work by exploring a model&#39;s parameter space directly rather than navigating the action space as reinforcement learning does. In each iteration, the method generates multiple slightly perturbed versions of the model by adding Gaussian noise to its parameters. &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="mso-line-height-alt:150.0%;"&gt; These variations are evaluated based on a reward function, and the model is updated by aggregating the most successful perturbations. This process enables broad exploration &lt;b&gt;without relying on backpropagation&lt;/b&gt;, which reduces memory usage and supports parallel computation. &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="mso-line-height-alt:150.0%;"&gt; Instead of storing large noise matrices, the method uses random seeds to regenerate perturbations on the fly. Parameters are perturbed and restored layer by layer, minimizing peak GPU memory demand. &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="mso-line-height-alt:150.0%;"&gt; Reward scores are normalized within each iteration to maintain consistent scaling, and the update step is simplified by absorbing the noise scale into the learning rate. These design choices make it feasible to apply evolution strategies to models with billions of parameters using surprisingly small population sizes. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="evaluation-and-performance-of-evolu" 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;mso-line-height-alt:125.0%;"&gt;Evaluation and Performance of Evolution Strategies in LLM Fine-Tuning&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="mso-line-height-alt:150.0%;"&gt; In experiments on the Countdown reasoning task, evolution strategies consistently outperformed reinforcement learning methods like PPO and GRPO across a range of model families and sizes. For example, on smaller models such as Qwen2.5-0.5B, where reinforcement learning failed to show improvement, evolution strategies increased accuracy significantly. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="padding-bottom:20px;padding-left:15px;padding-right:15px;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:626px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/210c01a1-56a0-4627-a4e3-6252b4ddfdc0/image.png?t=1760460534" alt="" height="auto" width="626" 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="mso-line-height-alt:150.0%;"&gt; Evolution strategies achieved a better trade-off between reward and KL divergence (a measure of how much the fine-tuned model deviates from the original) without needing an explicit penalty term. It also showed greater consistency across multiple runs and did not exhibit reward hacking, which is a common issue in reinforcement learning where models generate nonsensical but high-reward outputs. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class="btn_row"&gt;&lt;td 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 width="100%" role="none" border="0" cellspacing="0" cellpadding="0" style="margin:14px auto 14px auto;"&gt;&lt;tr&gt;&lt;td align="center" valign="middle"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tr&gt;&lt;td style="background-color:#2C81E5;border-radius:8px;mso-padding-alt:14px 20px;" class="btn"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92aBk6raLmPmiR2AmNB5EX1sfiHHJoxrHQX684mShSHuWQlcZY1POxLSYMhK8jzVvDJyXD9ETE9ioXm3RRWx2iDWgWvSVhx9Hwp4cXcVJ5mVeV0wjb-8GFG-9VsM1BSpZf4VmH0vmGynhuWs0vC54c0uVobf43V-gV5rBBFYW-OifTKoi__BUiWisapoA7VR0jJxUbQd8fflAkAFSMzSWlhzCLZgapgiwfS2EMHRnaHT9g/4kq/xrMKEwoGRY227W7i8vIjGQ/h12/h001.JECerAbwAwiXXWeAfUChBokTO-uywc0Sa6BzLaU6saE" target="_blank" rel="noopener noreferrer nofollow" style="background-color:#2C81E5;border-radius:8px;color:#FFFFFF;display:inline-block;font-family:'Open Sans','Segoe UI','Apple SD Gothic Neo','Lucida Grande','Lucida Sans Unicode',sans-serif;font-size:16px;font-weight:normal;line-height:18px;padding:14px 20px;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;/table&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td class="dd" align="center" valign="top" style="padding:20px;"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmG-FNB0qrv5ZIj7K3on2sYRwUIBc1pibSQDQLNOh7AZF-pGkADg6XVziteoncVuL6WNQo8xDtxPVA-3Fb76bKh_pDFFaqx0p6C5mVeg9fS1othW4449i_6GT96pFMcGnHkwLKgVv9TgdZYAa0P1xEQbnpjDKc6NAHYXW4w8a0WgWQE5rriGH6ErChtzKvp7KdvrPF3C2b1716T5iUwNJ80TL57qw3fP1xYNBmALAZDpkiXeA-EkMnCFsmFxApxT8dEIzM02IPMZVks25PKOzD-0/4kq/xrMKEwoGRY227W7i8vIjGQ/h13/h001.XaYIEd6Fwrj7FbFcQN5JAwFq9Jb9Z_FaO4897DExc9I" style="text-decoration:none;"&gt;&lt;table align="center" width="100%" cellpadding="0" cellspacing="0" border="0" role="none" style="max-width:520px;margin:0 auto;"&gt;&lt;tr&gt;&lt;td class="p" width="100%" style="padding:2px;border:none;"&gt;&lt;table width="100%" cellpadding="0" cellspacing="0" border="0" role="none"&gt;&lt;tr&gt;&lt;td align="center" valign="top" style="width:100%;"&gt;&lt;div style="max-height:0;position:relative;opacity:0.999;width:100%;mso-hide:all;"&gt;&lt;div style="display:inline-block;width:100%;padding-top:25%;"&gt;&lt;img width="20%" height="auto" loading="lazy" alt="" style="border:0;" src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/static_assets/youtube_play_icon.png"/&gt;&lt;/div&gt;&lt;/div&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.amatuKKICSickUKplYJXmG-FNB0qrv5ZIj7K3on2sYRwUIBc1pibSQDQLNOh7AZF-pGkADg6XVziteoncVuL6WNQo8xDtxPVA-3Fb76bKh_pDFFaqx0p6C5mVeg9fS1othW4449i_6GT96pFMcGnHkwLKgVv9TgdZYAa0P1xEQbnpjDKc6NAHYXW4w8a0WgWQE5rriGH6ErChtzKvp7KdvrPF3C2b1716T5iUwNJ80SB1N7lTp-5yPG5jwb2b7PPAaSItkwY47_fmOBxcnke2ZspFjWqwmITeW2XpUh09v8/4kq/xrMKEwoGRY227W7i8vIjGQ/h14/h001.NldQRVJ7lr40n8ZL8iF3EkMTJzuXDzkK5djroCny7_k" style="text-decoration:none;"&gt;&lt;img src="https://i.ytimg.com/vi/4BhZZYg2_J4/maxresdefault.jpg" width="480" height="auto" loading="lazy" alt="YouTube video by bycloud" style="display:block;height:auto;border:0;outline:none;text-decoration:none;background-color:#000000;width:100%;"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;p style="font-size:12px;font-weight:500;font-style:italic;font-family:Helvetica, Calibri, sans-serif;color: #686a6d; padding-top:0 !important;padding-bottom:6px !important; padding-left:4px !important;"&gt; Why would anyone let LLMs predict 4 tokens at once? 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