Email: Training Agents Inside of Scalable World Models

Subject:
Training Agents Inside of Scalable World Models
Received:
10/7/2025, 5:41:58 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 Polychromic Objectives for Reinforcement Learning and Stochastic activations &#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#160;&#8204;&#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 07, 2025 &nbsp; | &nbsp; &lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.c6q0w4g5sodbtO4I1B_pxSdB5RCIH6yy1Fm1CYma3EzYxKDC8yJqRsf_5e1gzhSqgUcjdNru3ohxDr6ggsMTJSGP1DCsCYD2wgCjXInxoXVjgY96vJew103YqM9QR6SoCrn8sp7Uco-j6_5c5tEBy7kVxML-sJF1lO9bFS_Hes-51TZKLh2MLZO_DK7DNWFlkOT1TeXhdq3vd2T_xbsgE9BHG4c5xB7imwBneV4ItHA85Mktd0ee2M-J4nmc9gbkZulsbWQLLYle6z5LRTIevUcdcOnvc25EhXwb4pjVMhmMC-GbtFJkNU6sdCZGL1mDFBDivRnxr5rjUedNZeb7c6EG7eZplVapZ4JOBvLwkNVr0xsmGevgD9EYaYcBNJAs61jwu9s2QpgS1EZu1-N_rSQDS2mJLdRvprqkWaJcr0CCYStlYEHcSWSUeDTsqncwMFIauSTmaauF3p3pch-CM7YkBDkrHPtmxv5I_oaS5ao7Nw2w799OaonzUb7_w6KFP-_CfGGr2YkGGV6IgoJFTdK3qy3tG6g4vhXOH0mF4KpiFSmTZ7VnGh4_GGaYZmXeBnrb0yXavNH7rqGqOeXt7c2CrMSmqipZEMM82A_prMcYA65xos5KX8j6-Eq3ubkPQ2DoUcLHltUBUMyBugQ_p6zxuybRmpEDhuibodwya88UdJd24nDi6Wr5qw576G4Oa7uRHdGNtjWE2Snry0dZE55YfTmYajNNyqKrB-wAyI6iG-gtvDVKn4-jFQfEaOl3t-kIVBwJCv2Ac-XYtwyYZ9nLHw5VzPof7sOAS5u0ztfreywM9sTLXRLACboeO21O63d3XknB5AHlBixaD_B8tw/4kj/GbeUl1zbTuO24U6eh_os5Q/h0/h001.ZpMEe8MU264GWNSzrOEkxP0TybpHg9OUQQnGyN-lSfI"&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; Training Agents Inside of Scalable World Models &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 Polychromic Objectives for Reinforcement Learning and Stochastic activations &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/4kj/GbeUl1zbTuO24U6eh_os5Q/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;Sep 29th ~ Oct 6th&lt;/i&gt;&lt;br&gt;&lt;i&gt;#76 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;♥ 5.2k&lt;/span&gt;&lt;/span&gt; Zhipu AI has released GLM-4.6, a new LLM with a longer context window of 200K tokens, superior coding performance, and advanced reasoning capabilities. GLM-4.6 also has more capable agentic functions and an improved writing style that better aligns with human preferences. You can try out GLM-4.6 on the &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.ZsobsZmG6kUZ4LjqczYBVKVDlAmESQcL-bONs-iLk77GSKR5tNm7R1KP_-Y9txPTkAniDD_eLzm6jAmlXXjzHJ2S-qoUiPJdOw_Jre3ui9RR8tI8QlkoNLSv4IokU1Kexo9oMsOn246lryYxmfGhVzYkJUHdut1jV-Oz-zxEcwqCazawsUBeHudbmBExF6Yem3HdhIMOQw-PVMyXW-av9soTFO1SUkoZmV_JGFa6FaHjq4j9EAMj7WGn4smxx92b/4kj/GbeUl1zbTuO24U6eh_os5Q/h1/h001.AuIEUKIouwmImbaUZ1s9Q4oTJjRW4DrbDWiSOEDYkjo" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Z.ai website&lt;/span&gt;&lt;/a&gt;, use it &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpSrFFzDetpQb3JmVcgzZn5b5izuuoOf2dGk4pNKDNQwh1-SJS_weGPv480bIIwOwoP8yqpRL_FF53jmJQId-vJIyUeNfuM3zF5PqhDYKNdQiy1g1yY9uPVaTidu_cUDEffUleR4vF5-wLSNVxIwW3LyEu37rq71aHU3pP9I6TGe5opFoMJK5PhL4PA0eXIDad5XrCK0njE6HtYAlk7-UNpU-WgjzhqTHlZHuEZuDFmoZ-y2pnzzj3olLXXeHfv236A/4kj/GbeUl1zbTuO24U6eh_os5Q/h2/h001.dL2kvyFMJlZrdJRk6sT_1Mav378lLfpkxI9qSydQ3So" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;via their API&lt;/span&gt;&lt;/a&gt;, or &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWr6LdnAmKyWSp5j0x8wnkgGqldVpXBIMyvs9SNhydLeqa1iw9QQmtKBwk8KAcX6teZWktuL-865sAuw4UpnwAELCFGzzwscx8nx_-_MnTgLH9y48nRDK5sFFNSeHTXGeWscJ29iz_WJuwz8IvnJwYZe1LibUtrGsSx8MMPsWFVlhkgm7REx9ZekcaPXEJua9GaHdmxFm03cKbpRSIwkeaS8hYyS3kNQmLAh6W3Tt1N77EYSELsSBTzn-02ZxsgLRsA/4kj/GbeUl1zbTuO24U6eh_os5Q/h3/h001.iaCIzDj-SSlNvDX56JEOtXMjTcuu3U7rJNGuoYI20TY" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;download the model weights&lt;/span&gt;&lt;/a&gt; from Hugging Face to run it locally. &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/3637a68d-fb97-4345-a703-3ea07701b37b/coding_benchmark.png?t=1759857231" 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;♥ 11k&lt;/span&gt;&lt;/span&gt; IBM has introduced &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j259JbYiT19QpsBdhuHcIW0StLGLb321gBTrH8yRkCuJuBNbYybCfI4l6rF43pA1RWm6k9wZcIYliCyAhuqnO8Z-SmzxTKYGeDemf-_YM_DtrVZsC7V4yW3C9KWRAy8E55j2XJs-EVZucQ2GCulkXd2X9aE9s_XjbMbtb2CdeWs8DGJ3Oz99e7hT1rNpPAeh7df_h0DpUipSNsrTu1o81gBPmT6dgrsj5zh7SaKqaw_SDFMGzPXYtsmSkmIzknd-Qw1AG91oV-c4whPGXFMxLlQinJoOVf32l5oXp_5JfszxqmUYCuCKr6rfJMVVhja_JdjWZwl7qb3sLQugQsuHCjlQ/4kj/GbeUl1zbTuO24U6eh_os5Q/h4/h001.u_AvNojeQaZsR7wwZORpzB4U-18LSMPvU3BalapeDec" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Granite 4.0&lt;/span&gt;&lt;/a&gt;, a new generation of hyper-efficient, high-performance hybrid models that use a &lt;b&gt;Mamba/transformer architecture&lt;/b&gt;, which significantly reduces memory requirements. The Granite 4.0 models are open-sourced under an &lt;b&gt;Apache 2.0 license&lt;/b&gt;, and they are the first open models to receive &lt;b&gt;ISO 42001 certification&lt;/b&gt; and be cryptographically signed, ensuring their security and trustworthiness. You can access Granite 4.0 on IBM &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j26wmLriuTd7tPlt55uDRV88qFdMdMAifWdRyQPXOkGcglcOHyyerasbOpvcpOEE2sWRDNy4rS6woji6yevtlkk_pYMvVhrVwg9lltuJJ0fneoTF2KM1K69X5-GnU-R49u1WWxTVFFWsCgRytZd37nHZfcHZVgMmKyIu5NwAnIQf6h3yMTjLLvpy0DfBg6tzAyXPcoJRXB0qbIJZe0KadiN-WMbPifdRw1upEpAYcc0ZARCDE_reJBd-F_ErFFqgJyA/4kj/GbeUl1zbTuO24U6eh_os5Q/h5/h001.cKr6zNyBlNl5mUXux1gkcTjNHG4xsExPfPg-yEY7gAY" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;watsonx.ai&lt;/span&gt;&lt;/a&gt; or via platform partners, including Hugging Face, Docker Hub, and NVIDIA NIM. &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/6b64ff65-db35-489b-960b-0157272f96fd/image_1363588221?t=1759857470" 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;♥ 8.3k&lt;/span&gt;&lt;/span&gt; &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DUiN96-Eq7pUHzwEhy5j2wWIFm9TgZ3SHguHucUZoaV5A9Q8ff-AHDtQUdCK2jt1d68npQxVK3-0P7ak8pA9hYIspDpD2UJjGb86--sDIQiFrN5-Es2s6EvWlR5EnNLy057q1zeZVE-ibV0hn3HMU31UXjZOm90lRrOyNOuPsy0YnmFplGoEVCezPn2V815cOaZbCGJMexXRzOkOKSzCbRPOZyhpNwOtMluddbGHP_WmgNQ2Kr5wYqnK2S4xjtYTMkGcUpVILxAHdspVsq_h4Q/4kj/GbeUl1zbTuO24U6eh_os5Q/h6/h001.Z9KyRp3h9_NqhC-M4rxAeUa3HcZ63KBdTWnFx5BcC48" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Perplexity has launched Comet&lt;/span&gt;&lt;/a&gt;, a new browser with a built-in personal AI assistant designed to work for you. Comet can help you with a variety of tasks, such as understanding how different news outlets are covering a topic, organizing your tabs, drafting emails, and even building a basic website. &lt;/p&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;♥ 2.4k&lt;/span&gt;&lt;/span&gt; Tencent has released &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWkSfgzu7eg1o_HUVTBLBU_tAf_VQ2-zcXBDviTfibeid4w_t13OaSw2JxQgq4i4OJf8Gh09-N67kmYzlhtI1qrxIS40goYIqd_k8IKPAhQYA32SrgyXU78BzxY6wIbX16cXtZOYrNc0VNEpWHSstq46eJYk4s00M_O_xW17vFE7VKfJcN5FETYe04wtrDD07kAYwF-ck0Uuv4qwq7ID2pamp3cjU8TWNUgZhB3qsBI9l/4kj/GbeUl1zbTuO24U6eh_os5Q/h7/h001.Hw0Utelb_pBzlpe5dJlTAGfIinNZ8yniqQlEGV7h65I" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Hunyuan Image 3.0&lt;/span&gt;&lt;/a&gt;, and this new version has enhanced dual encoders and advanced RLHF optimization to produce stunning, high-quality images. Hunyuan Image 3.0 &lt;b&gt;supports both Chinese and English prompts&lt;/b&gt; and offers flexible aspect ratios for your creative projects. You can get started or see a demo on the &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.CxDkkVpJsBdVoe83c_tBWkSfgzu7eg1o_HUVTBLBU_tAf_VQ2-zcXBDviTfibeid4w_t13OaSw2JxQgq4i4OJf8Gh09-N67kmYzlhtI1qrxIS40goYIqd_k8IKPAhQYA32SrgyXU78BzxY6wIbX16cXtZOYrNc0VNEpWHSstq46eJYk4s00M_O_xW17vFE7Vv64M5bqPfQimuDQBkxnueaFSsljX_kJTvgcOLG2ToJK3qSfTM7SetrE_O4BT4ZAJ/4kj/GbeUl1zbTuO24U6eh_os5Q/h8/h001.kxroFuiF8ONfU9-vfc-wsKGMs6XVFndgnHYyz2CixZM" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Hunyuan Image website&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:438px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/6cf6a3b4-1565-46bc-ba84-664356e8138d/image.png?t=1759857757" alt="" height="auto" width="438" 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;♥ 14k&lt;/span&gt;&lt;/span&gt; &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.DIqyAo9xTeoWriogq2VlWeUmi9WmFR4pnC4wMSHAHOEi0s_79rCOu0Ids4VjFrRaM6MGxOzAW-171L7McQM2kFFjCnBfNv6K5qliP9b95f06NXlb7AM1Ayeq_q_gOU5SxlUWZ7mJT331E8juP62sfBQtajRLkJhjcptudllExZEp8NzXiPBA8x6iSOQnZ9WnRjBGebfYe9LzZbxayzqXW1SZF1kKoP1OKTKkLQ5Q23wYa7ukDG-pAcx-SapAhTR4vRjyDTKU3lTLaiklLQQpWA/4kj/GbeUl1zbTuO24U6eh_os5Q/h9/h001.ie2zBqjL_UilAv-9Gcj5PFoJQfPhsNXhzyjowb9eDWE" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;OpenAI has announced Sora 2&lt;/span&gt;&lt;/a&gt;, a video and audio generation model that can generate videos with synchronized dialogue and sound effects. It can even insert real-world people, animals, or objects into generated scenes with remarkable fidelity. You can use Sora 2 by downloading the new &quot;Sora&quot; social iOS app, which is now available in the U.S. and Canada. &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:500px;"&gt;&lt;img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/2fd39c28-6a76-4f7b-a55d-56ca5a71d9f3/image.png?t=1759857984" alt="" height="auto" width="500" 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;♥ 425&lt;/span&gt;&lt;/span&gt; &lt;a class="link" href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpTanKmQxfkMnn7OJKz54OPHbrSKDY2zKc0fKntGskBM0wOo3QArklugneX51NxGG_sCXszyYZk2ALwTvg0nbvC8GkJl2GXrZkBA_bu4PJep2wA1AfDWjVqoJ_xA4ukK2t7H3I-0sB2BctHuq7KOBXwk3sKAtqg1V0bIlTh6ZC6Jx5z8CWGBZr8iDrdD_fMRgVFPZq4J--jOqlx_yQao2x3M4fKHGvNjzOHD9iqFU74sxXPCpHq-n0U3tADjJ1aQiM3tUMcn-3n65wDn-76d3ObBumt72mtKeUPwY0RRWlk6JMa0BePl29E7Zd9OOlM09aBeNlVa4Snzezu3KhwZcxATNSrfrIEG_CwTS1x7bLYAl/4kj/GbeUl1zbTuO24U6eh_os5Q/h10/h001.4vcOkO_GvPdzNmYe7kDnGHygzpcnsm9pLY9T1Wyj-T0" target="_blank" rel="noopener noreferrer nofollow"&gt;&lt;span&gt;Google has launched Jules Tools&lt;/span&gt;&lt;/a&gt;, which is a command-line interface for their asynchronous coding agent, Jules. This tool allows developers to interact with Jules directly from their terminal to perform tasks like writing tests, building new features, and fixing bugs. You can install Jules Tools via npm by installing the &lt;code&gt;@google/jules&lt;/code&gt; package. &lt;/p&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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et al. [&lt;/i&gt;&lt;/span&gt;&lt;i&gt;Google DeepMind&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; ♥ 485 &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 Wordl Models &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="introduction-to-world-models-and-dr" 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 World Models and Dreamer 4&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; We all know about language models, but that’s not the only kind of model out there. Researchers have developed world models, which learn from videos and simulate experiences to train intelligent behaviors. However, previous world models have struggled to accurately predict complex object interactions, especially in rich environments like video games. &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 Dreamer 4, which addresses this challenge by learning a fast and accurate world model that enables reinforcement learning inside simulated experience. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class="embed-gen-img-r"&gt;&lt;td align="center" valign="top" style="padding:12px 27px 12px 27px;" class="dd"&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" class="o" style="padding:12px 12px 12px 12px;;background-color:#FFFFFF;border-color:#F1F1F1;border-radius:5px 5px 5px 5px;border-width:1px 1px 1px 1px;"&gt;&lt;!--[if !mso]&gt;&lt;!--&gt;&lt;div style="display:none; float:left; overflow:hidden; width:0; max-height:0; line-height:0;" class="mob-show"&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"&gt;&lt;tr&gt;&lt;td align="center" valign="top"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpQSxa46C2NVBKURDNuOjDh-TvqwJc6MoxM0Gg_hOzK5COBXdj98oGsQK_rZNjQ26DtdgcslmN5FPyY7_-uggqYDFLUjeFx4gSb9q1GiCchJ-7hPG0r3GWNaSAlBMC1BVASIMRBLjUibrtJKcHEhzQhQfvO6GtBWose2filvqm9Xh7bEi256YMyU1_Y_wFT7R5wrSdBlFuJUEGiN94wd9uF2R2H_M0CzRRJ2dy6avS8A9785aW9FdkClcJ9XfkZWnSg/4kj/GbeUl1zbTuO24U6eh_os5Q/h15/h001.XD4cyqDIrDLpVzusL8GdPucBR0Nc1-7eC300w_MyorI" target="_blank"&gt;&lt;img src="" width="100%" style="height:auto;display:block;"/&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td height="16" style="font-size:16px;line-height:16px;"&gt;&nbsp;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;!--&lt;![endif]--&gt;&lt;table role="none" border="0" cellspacing="0" cellpadding="0" align="right" width="100%"&gt;&lt;tr&gt;&lt;td width="57%" align="center" valign="middle" class="mob-stack"&gt;&lt;table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"&gt;&lt;tr&gt;&lt;td align="left" valign="middle" class="l"&gt;&lt;p&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpQSxa46C2NVBKURDNuOjDh-TvqwJc6MoxM0Gg_hOzK5COBXdj98oGsQK_rZNjQ26DtdgcslmN5FPyY7_-uggqYDFLUjeFx4gSb9q1GiCchJ-7hPG0r3GWNaSAlBMC1BVASIMRBLjUibrtJKcHEhzQhQfvO6GtBWose2filvqm9Xh7bEi256YMyU1_Y_wFT7R51Hfi5mFhGW9lnXEWZDXViI1aaRwMohvPvAvf7L1OqBbm4b-ELIDmsmRDAI0ozISyw/4kj/GbeUl1zbTuO24U6eh_os5Q/h16/h001.iyKtBdcXZxBXVx6y66dnDV2JDQ8MiDElvhsRTn51QbQ" style="text-decoration:none;font-style:normal;color:#2D2D2D !important;font-size:14px;line-height:20px;" target="_blank"&gt; Training Agents Inside of Scalable World Models &lt;tr&gt;&lt;td align="left" valign="top" class="m"&gt;&lt;p style="font-size:13px;line-height:19px;color:#2D2D2D;"&gt; Introducing Dreamer 4 &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td align="left" valign="bottom" class="n" style="vertical-align:bottom;padding-top:12px;"&gt;&lt;p style="word-break:break-word;"&gt;danijar.com/project/dreamer4&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/a&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/td&gt;&lt;td width="3%" style="font-size:16px;line-height:16px;" class="mob-hide"&gt;&nbsp;&lt;/td&gt;&lt;td width="40%" align="left" valign="top" class="mob-hide"&gt;&lt;a href="https://elink4f7.mail.bycloud.ai/ss/c/u001.9ggl6Mt0xphuuMReR5gVpQSxa46C2NVBKURDNuOjDh-TvqwJc6MoxM0Gg_hOzK5COBXdj98oGsQK_rZNjQ26DtdgcslmN5FPyY7_-uggqYDFLUjeFx4gSb9q1GiCchJ-7hPG0r3GWNaSAlBMC1BVASIMRBLjUibrtJKcHEhzQhQfvO6GtBWose2filvqm9Xh7bEi256YMyU1_Y_wFT7R52LVypzN8M4-b8LM5__OPCJlHdvQ29IlyGaZFeFFAGIV9HIZrUgT38Yshrc1GFKx-Q/4kj/GbeUl1zbTuO24U6eh_os5Q/h17/h001.90-sxqVc8PTuh2IDJ02Y1Ww8G7Ev8BOvSOxLoLn0pQs" target="_blank"&gt;&lt;img src="" width="230" style="height:auto;display:block;"/&gt;&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 id="inner-workings-of-dreamer-4" 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 Workings of Dreamer 4&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; Dreamer 4 consists of two main components: a tokenizer and a dynamics model, both built on an efficient transformer architecture. The tokenizer compresses video frames into compact representations, while the dynamics model predicts future representations based on past actions and observations. &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/b92593b7-b0a8-41cb-81d0-c5d50f8f2ea9/image.png?t=1759852393" 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;World model design. Dreamer 4 consists of a causal tokenizer and an interactive dynamics model, which both use the same block-causal transformer architecture.&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; This setup allows the model to handle multiple modalities, like images and actions, within a single transformer. It uses a shortcut forcing objective, which helps the model generate frames quickly and accurately with just four sampling steps per frame. This approach reduces error accumulation over long video sequences and supports real-time inference on a single GPU. &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/415b77c1-87d6-412c-910d-0e89f8a2536f/image.png?t=1759852340" 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; The model is trained in three phases. First, the tokenizer and dynamics model are pretrained on videos, with or without action labels. Next, the model is fine-tuned with task-specific inputs to predict actions and rewards. Finally, the policy is improved through imagination training, where the agent practices decision-making inside the world model using reinforcement learning. &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/57bafb15-2a86-4d0f-9484-2c87cb4682a7/image.png?t=1759852368" 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;Agent performance in Minecraft without environment interaction. &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-dreamer-4" 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 Dreamer 4&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; Dreamer 4 was able to achieve a major milestone: it is the first agent to obtain diamonds in Minecraft using only offline data, without any environment interaction. This task requires executing over 20,000 low-level mouse and keyboard actions from raw pixels. Dreamer 4 significantly outperforms previous methods, such as OpenAI’s VPT agent, despite using &lt;b&gt;100 times less data&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 also surpasses agents that leverage large vision-language models like Gemma 3, nearly tripling the success rate for crafting iron pickaxes. The world model itself shows remarkable accuracy in predicting object interactions and game mechanics, successfully completing 14 out of 16 complex tasks in human-in-the-loop evaluations. &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/d6ce7302-fb6b-48ce-99d9-a76795b56d61/image.png?t=1759852479" 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;Action generalization.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&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.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92ZDCTLU_mLW-baYc7sqX1l7verPAbAHpAtffYqhzaGjmRrlf5NkTdrEEr37BTalFdKxTJ6QW4oDOkZ1QREpNIdFJkW9Pp1jyhOta5uU7V-60SQFw5r-Bqf42ovxhOj_ZXnAX8SM4GyTV7zE3KKm6pkXlPZXUBFk7XMgMK_HIBpaXEqKGIOE7heUZARhoGnqYgBARmcFF73r1N2qfxE9Ky-A9E9usCYXhOC9tt0rcFkVxA/4kj/GbeUl1zbTuO24U6eh_os5Q/h18/h001.ICzx1hyW35FzWrwvbPjIZpL1dvjFFQTE57QZBnG8cjQ" 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="stochastic-activations" 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;Stochastic activations&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;Lomeli et al. [Meta FAIR, Ecole Normale Supérieure Paris Saclay, Paris Cité University]&lt;/i&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; ♥ 22k &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 Activation Functions &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-stochastic-activati" 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 Stochastic Activations&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; Many AI architectures use ReLU activation because it produces sparse outputs (many zeros), which can speed up inference by reducing the number of calculations needed. However, ReLU has a known weakness: for negative inputs, its gradient is zero, which can halt learning in parts of the network during training. &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; On the other hand, the SILU activation avoids this problem and generally leads to better model accuracy, but it doesn’t produce sparsity. So, how can we get the best of both worlds? &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 stochastic activations. Instead of committing to one activation function, this approach randomly chooses between ReLU and SILU during training. &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/5354dab7-c1fe-4de6-addb-7a09f29e764b/image.png?t=1759854065" 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;Stochastic activation randomly selects one of two activations when x &lt; 0: (1) RELU selected with probability 1-p; otherwise (2) another activation, in particular SILU.&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-workings-of-stochastic-activa" 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 Workings of Stochastic Activations&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 core idea behind stochastic activations is to randomly switch between ReLU and SILU when the input to the activation function is negative. This is controlled by a Bernoulli random variable: with probability (p), the model uses SILU, and with probability (1-p), it uses ReLU. &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; For positive inputs, the function remains fixed, i.e., it is either the identity (like ReLU) or as SILU, depending on the configuration. This stochastic behavior during training ensures the network experiences both the smooth gradients of SILU, which help optimization, and the sparsity-inducing pattern of ReLU, which is useful later. &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 authors combined this technique with a fine-tuning step called Swi+FT to prepare the model for efficient inference. Here, the model is first pre-trained mostly with SILU or a stochastic mix, and then, for the last 5–10% of training steps, it switches entirely to ReLU. &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 final tuning phase adapts the weights to ReLU’s behavior, so the model performs well when ReLU is used at inference time. &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/af7ab321-565c-42b2-8d3c-15087d242fac/image.png?t=1759853926" 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;Losses during the last 500 steps of the training loss of LM1.&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-stochasti" 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 Stochastic Activations&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 researchers tested this method on models with 1.5 billion and 3 billion parameters, and evaluated it on a range of tasks, including code generation, common-sense reasoning, and question answering. &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; When using ReLU at inference time, models trained with stochastic activations and fine-tuning (Swi+FT) achieved validation losses close to those of SILU-trained models, and significantly better than models trained with ReLU alone. &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/25df734b-0e00-4afc-85bd-3ec36be74e25/image.png?t=1759854131" 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;Performance per benchmark of the RELU and SILU.&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; It was able to produce &lt;b&gt;1.65 times faster inference&lt;/b&gt; on a CPU, with around 90% of activation outputs being zero. This shows that the approach successfully balances accuracy and computational cost. Additionally, when stochastic activations were used directly during inference for text generation, they provided a way to sample diverse outputs. In some tasks, this stochastic generation &lt;b&gt;outperformed standard temperature sampling&lt;/b&gt;, though results varied across benchmarks. &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.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92alJxe5vTvLSL9GvS37oekahx1xliCfzsbSjE5FXmAcxVS69VWQc0NBNND14XT71i9CDS89RP8AndJ8eEnksijpcwG58jJxcSsmWls5PcEf1TqTPH3XcwP_UkdZxr0iOTgLXKbu6nxka9l1fEoubvU0w71kejQlig-6ojOGLYjC_OKBt4p5O58uVjGWi7NU9Nfi-9gyWrbSVg1ulsZy-KBrA6h0RtrFtNw3sVYABLevQg/4kj/GbeUl1zbTuO24U6eh_os5Q/h19/h001.RKwe_X-N0LJj4xoZvSdmBaWgVMVdBozRH2L-itik1Ew" 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="polychromic-objectives-for-reinforc" 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;Polychromic Objectives for 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;Hamid&lt;/i&gt;&lt;span style=""&gt;&lt;i&gt; et al. [&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; ♥ 424 &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 Reinforcement Learning &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-polychromic-objecti" 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 Polychromic Objectives in RLFT&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 fine-tuning is a powerful technique for tuning pretrained AI models, but it often comes with a hidden cost. As policies are refined for better performance, they can lose the rich diversity of behaviors they once had. &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 entropy collapse makes it harder for models to explore new solutions and adapt to unfamiliar tasks. To tackle this issue, researchers have developed polychromic objectives, a new approach that explicitly encourages policies to maintain and refine a wide range of behaviors during fine-tuning. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="inner-workings-of-polychromic-ppo" 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 Workings of Polychromic PPO&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; This method shifts the focus from optimizing individual trajectories to evaluating entire sets of them. Instead of rewarding a single successful path, the policy is trained to generate groups of trajectories that collectively exhibit high performance and diversity. This broader perspective helps prevent the model from over-specializing on just a few high-reward actions. &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/420a76ac-4968-4708-9f3e-7d2eab692859/image.png?t=1759854763" 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; This reinforcement learning approach uses the polychromic objective, which scores a set based on both the average reward of its trajectories and a measure of their diversity. For example, in grid-world tasks, diversity might be defined by whether trajectories visit different rooms, while in algorithmic tasks, it could involve generating unique sequences. By combining these elements, the objective ensures that policies are incentivized to explore varied strategies without sacrificing success. &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; To implement this in practice, the researchers adapted proximal policy optimization (PPO) into polychromic PPO. They use vine sampling to collect multiple rollouts from key states during training, and then compute a shared advantage signal for all trajectories in a set. This means that every action in a diverse and successful set receives the same positive update, reinforcing the policy to produce such sets consistently. The result is a fine-tuning process that naturally balances exploitation of known good behaviors with exploration of new ones. &lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td id="evaluation-and-results-of-polychrom" 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 Polychromic PPO&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 researchers tested this approach on BabyAI, Minigrid, and Algorithmic Creativity tasks, and the results show that polychromic PPO achieves higher success rates and better coverage of different environment configurations compared to standard RL methods. In pass@k evaluations, where models get multiple attempts to solve a task, polychromic PPO shows substantially improved performance as k increases. In challenging tasks like Bosslevel, it achieved up to &lt;b&gt;15% higher pass rates&lt;/b&gt; with more attempts, while baselines plateaued early. &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/e8b9e220-572d-4a40-9c01-ab34078ba291/image.png?t=1759854783" 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;Average reward and success rate (%) on BabyAI tasks.&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; While there is a slight trade-off in some validity metrics, the overall gains in creativity and coverage make polychromic objectives a promising direction for future AI systems that need to explore and innovate. &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/6ac135d9-e483-4769-911d-72db3d2d6db8/image.png?t=1759854835" 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;Average pass rate (%) in one attempt on BabyAI tasks under large initial-state perturbations.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&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.fUNb4GdFo9D3F8WuLArtoV5sElgytBlvJRzI9WtI92a0IWcKNjHgG7_a8MncsqclTP7ROTN0iuHDi558QNGPr_kW6u7AdrXtDV91XwFUh2xbaPcVbJ4_eqOUCzLgimnAcSPO-TaJEMLhKNj9bkiLyiamgrEozzZIPm8WL-WjQaeASWtlZjNK1Td60705St03pE5f5t0ylj-tDLZbLxpfIqZ_3OZ-fAAS7TscCqq1-crGwolN-E5JykTU8YXUiBmWF6CxlzFAliuZGaVfLrGvhQ/4kj/GbeUl1zbTuO24U6eh_os5Q/h20/h001.rDQh1vsthteUUiDMYjAd8tRAFcn_a37BXslJ4yJwEpQ" 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; 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