{"id":1670,"date":"2026-06-29T04:00:00","date_gmt":"2026-06-29T04:00:00","guid":{"rendered":"https:\/\/futurenews24.com\/index.php\/2026\/06\/29\/2606-22600\/"},"modified":"2026-06-30T02:59:16","modified_gmt":"2026-06-30T02:59:16","slug":"2606-22600","status":"publish","type":"post","link":"https:\/\/futurenews24.com\/index.php\/2026\/06\/29\/2606-22600\/","title":{"rendered":"[2606.22600] On the Place Bias of On-Coverage Distillation"},"content":{"rendered":"<p><br \/>\n<\/p>\n<div id=\"content-inner\">\n<div id=\"abs\">\n<div class=\"dateline\">\n  [Submitted on 21 Jun 2026 (v1), last revised 26 Jun 2026 (this version, v3)]<\/div>\n<p>View a PDF of the paper titled On the Place Bias of On-Coverage Distillation, by Yan Xie and 4 different authors<\/p>\n<p>    View PDF<br \/>\n    HTML (experimental)<\/p>\n<blockquote class=\"abstract mathjax\"><p>\n            <span class=\"descriptor\">Summary:<\/span>On-Coverage Distillation (OPD) improves the training effectivity of normal reinforcement studying by means of dense, token-level supervision from academics. In the usual KL goal of OPD, token-level losses are uniformly averaged, implying equal weights for all tokens. Nevertheless, we uncover that not all tokens are created equal: as pupil rollouts develop longer, they deviate farther from the trainer&#8217;s distribution, resulting in degraded supervision high quality at later positions. In consequence, OPD utilizing solely the primary 30% of tokens can carry out comparably to utilizing all tokens, whereas OPD utilizing solely the final 30% of tokens barely learns something. On this work, we offer a principled understanding of this challenge by means of the lens of constrained optimization. Primarily based on these insights, we derive Significance-Weighted On-Coverage Distillation (IW-OPD), during which the load assigned to every token is determined by the accrued discrepancy between the scholar&#8217;s and trainer&#8217;s distributions, naturally upweighting earlier tokens and downweighting later ones with bigger deviations. We present that IW-OPD converges considerably sooner than OPD, with higher studying effectivity, and achieves higher closing efficiency than normal OPD in each same-size and cross-scale settings, enhancing efficiency as much as 6.9 factors on AIME-2025.\n    <\/p><\/blockquote><\/div>\n<\/div>\n<div>\n<h2>Submission historical past<\/h2>\n<p> From: Yan Xie [view email]                  [v1]<br \/>\n        Solar, 21 Jun 2026 17:20:21 UTC (202 KB)<br \/>\n            [v2]<br \/>\n        Tue, 23 Jun 2026 06:08:09 UTC (202 KB)<br \/>\n    [v3]<br \/>\n        Fri, 26 Jun 2026 02:45:32 UTC (202 KB)\n<\/p><\/div>\n<p><br \/>\n<br \/><a href=\"https:\/\/arxiv.org\/abs\/2606.22600\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>[Submitted on 21 Jun 2026 (v1), last revised 26 Jun 2026 (this version, v3)] View a PDF of the paper titled On the Place Bias of On-Coverage Distillation, by Yan Xie and 4 different authors View PDF HTML (experimental) Summary:On-Coverage Distillation (OPD) improves the training effectivity of normal reinforcement studying by means of dense, token-level [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1672,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"http:\/\/arxiv.org\/static\/browse\/0.3.4\/images\/arxiv-logo-fb.png","fifu_image_alt":"","jnews-multi-image_gallery":[],"jnews_single_post":[],"jnews_primary_category":[],"jnews_override_bookmark_settings":[],"jnews_social_meta":[],"jnews_override_counter":[],"footnotes":""},"categories":[2],"tags":[1427,2134,2133,330],"class_list":["post-1670","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-research-breakthroughs","tag-bias","tag-distillation","tag-onpolicy","tag-position"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - 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