[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-training-method-mixes-rl-and-distillation-for-llms":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},8720,"new-training-method-mixes-rl-and-distillation-for-llms","New Training Method Mixes RL and Distillation for LLMs","Distilled RL blends reinforcement learning with teacher guidance so AI models can learn from very different AI models, not just close relatives.","A new paper tackles a stubborn problem in how AI models are taught after their initial training: learning from a very different teacher model without blindly copying it.\n\nPost-training for large language models usually leans on one of two methods. Reinforcement learning judges whole outputs as right or wrong, which makes it hard to figure out which specific step in a chain of reasoning deserves credit or blame. On-policy distillation instead has a student copy a teacher's token-by-token probabilities, but that only works when the teacher is similar enough to the student already; a very different teacher tends to give confusing signal. The paper's method, called Distilled RL, folds teacher guidance directly into the RL objective using three techniques, reverse importance sampling with clipping, negative sample reset, and sequence-level geometric normalization, so the student pulls in new knowledge selectively instead of imitating everything. Across both same-family and cross-family teacher-student pairs, the authors report it beat standard RL and on-policy distillation on both first-try accuracy (pass@1) and best-of-many accuracy (pass@k).\n\nThe cross-family result is the notable part. Most current distillation setups only work well when teacher and student come from the same model family, because mismatched teachers confuse standard methods. If this approach holds up beyond the paper's own tests, it could let smaller or open-source models draw selectively on larger or differently-built teachers, instead of facing an all-or-nothing choice.\n\nThat's still an if. This is one arXiv paper with author-released code, not a method any major lab has shipped, and self-reported benchmark gains have a habit of shrinking once outside teams try to reproduce them.","[\"distillation\",\"reinforcement-learning\",\"llm-training\",\"ai-research\"]","2026-09-30T04:00:00.000Z","2026-09-30T21:40:47.436Z","2026-09-30T21:40:53.194Z","published",null,[],"ai",[26,27,28,29],"distillation","reinforcement-learning","llm-training","ai-research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17247",0,{"sections":36},[37,41,46,51,56,60,64,68,73,77,82,87,92,97],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",5214,"2026-09-30T13:00:00.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":45},"Security","security",793,"2026-09-30T12:55:00.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":50},"Policy","policy",419,"2026-09-30T12:24:32.000Z",{"name":52,"slug":53,"count":54,"latest_published_at":55},"Deals","deals",291,"2026-09-30T10:38:22.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":40},"Hardware","hardware",196,{"name":61,"slug":62,"count":63,"latest_published_at":18},"Science","science",155,{"name":65,"slug":66,"count":67,"latest_published_at":40},"Consumer Tech","consumer-tech",144,{"name":69,"slug":70,"count":71,"latest_published_at":72},"Dev Tools","dev-tools",91,"2026-09-30T12:58:00.000Z",{"name":74,"slug":75,"count":71,"latest_published_at":76},"Software","software","2026-09-25T20:55:00.000Z",{"name":78,"slug":79,"count":80,"latest_published_at":81},"Startups","startups",83,"2026-09-29T21:51:36.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"General","general",49,"2026-09-28T16:44:57.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"Gaming","gaming",48,"2026-09-25T18:35:21.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",31,"2026-09-28T14:31:34.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]