[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-on-policy-warmup-helps-ai-agents-learn-rewards-faster":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},9144,"on-policy-warmup-helps-ai-agents-learn-rewards-faster","On-Policy Warmup Helps AI Agents Learn Rewards Faster","A new warmup stage lets agents imitate a teacher on their own messy attempts before reinforcement learning begins, and that jumpstarts reward discovery.","A new paper tackles one of reinforcement learning's oldest headaches: agents that get almost no feedback when training starts.\n\nReinforcement learning with verifiable reward, or RLVR, trains language-model agents by rewarding them only when they reach a verifiably correct outcome, which means little signal early on. The researchers tested on-policy warmup, where a teacher model supervises the student on trajectories the student itself generates, mistakes, dead ends, and recoveries included, before RLVR training begins. That differs from standard imitation learning, which trains on clean, teacher-generated demonstrations rather than the student's own missteps. Agents warmed up this way hit strong performance earlier in training and finished higher than agents trained without it, a pattern the paper calls the on-policy acceleration phenomenon.\n\nMost work on reinforcement learning for agents focuses on reward shaping or throwing more compute at the problem. This paper argues the real bottleneck is simpler: an agent that never stumbles into a rewarded trajectory early has nothing to learn from, and walking it through its own likely mistakes first fixes that. The paper backs the claim with a theoretical link between on-policy distillation and trajectory-level distribution matching, giving bounds on how fast reward discovery happens.\n\nIt is a controlled comparison, not a deployed agent, so whether on-policy warmup survives messier real-world tasks, where mistakes do not come with a tidy recovery path, is still an open question.","[\"reinforcement-learning\",\"ai-agents\",\"llm-training\",\"ai-research\"]","2026-10-01T04:00:00.000Z","2026-10-01T22:16:32.583Z","2026-10-01T22:16:36.437Z","published",null,[],"ai",[26,27,28,29],"reinforcement-learning","ai-agents","llm-training","ai-research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.39436",0,{"sections":36},[37,40,44,48,53,58,62,67,72,76,81,86,91,96],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",5572,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",815,{"name":45,"slug":46,"count":47,"latest_published_at":18},"Policy","policy",430,{"name":49,"slug":50,"count":51,"latest_published_at":52},"Deals","deals",298,"2026-09-30T21:00:26.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":57},"Hardware","hardware",196,"2026-09-30T13:00:00.000Z",{"name":59,"slug":60,"count":61,"latest_published_at":18},"Science","science",163,{"name":63,"slug":64,"count":65,"latest_published_at":66},"Consumer Tech","consumer-tech",149,"2026-09-30T22:57:11.000Z",{"name":68,"slug":69,"count":70,"latest_published_at":71},"Dev Tools","dev-tools",93,"2026-10-01T02:30:48.000Z",{"name":73,"slug":74,"count":70,"latest_published_at":75},"Software","software","2026-09-30T21:41:11.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Startups","startups",84,"2026-09-30T20:39:09.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Gaming","gaming",51,"2026-09-30T16:24:30.000Z",{"name":87,"slug":88,"count":89,"latest_published_at":90},"General","general",50,"2026-09-30T21:37:54.000Z",{"name":92,"slug":93,"count":94,"latest_published_at":95},"Reviews","reviews",31,"2026-09-28T14:31:34.000Z",{"name":97,"slug":98,"count":99,"latest_published_at":100},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]