[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-researchers-teach-ai-to-flip-bad-feedback-into-good-data":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},11111,"researchers-teach-ai-to-flip-bad-feedback-into-good-data","Researchers Teach AI to Flip Bad Feedback Into Good Data","A new reinforcement learning framework learns which feedback to trust, ignore, or invert, letting AI systems survive sabotaged training data.","A new reinforcement learning method figures out, on its own, which of your feedback sources are lying to you - and uses their lies against them.\n\nResearchers built a framework called TriTrust-PBRL that trains AI agents using human preference comparisons - the \"this one is better than that one\" judgments used to teach robots and other systems what good behavior looks like. Instead of assuming every annotator is honest, or trying to filter out noisy ones, the system assigns each contributor a trust score that can land anywhere from fully trusted to ignored to deliberately inverted. Those scores emerge automatically from the training math itself, with no manual labeling of who is reliable required. The team tested the approach on robotic manipulation tasks (MetaWorld) and locomotion tasks (DM Control), deliberately planting adversarial annotators who gave backwards preferences on purpose.\n\nThe system kept performing close to what a setup with perfect feedback would achieve, even with a chunk of its training data actively sabotaged, while standard preference-learning methods broke down under the same conditions. Preference-based training underlies a lot of current AI alignment work, including the reinforcement-learning-from-human-feedback approach used to steer large language models. A method that automatically spots and flips bad-faith raters, rather than just discarding them, is useful anywhere feedback pipelines mix reliable and unreliable sources - crowdworkers, red-teamers, or internal reviewers among them.\n\nIt is a narrower, tidier version of a problem every RLHF pipeline already has: some raters are wrong, and a few might be lying on purpose. This paper's trick is not catching bad actors - it is turning their bad signal into good signal, a neater fix than the usual filter-and-pray approach.","[\"reinforcement-learning\",\"rlhf\",\"robotics\",\"ai-research\"]","2026-10-09T04:00:00.000Z","2026-10-10T06:37:18.011Z","2026-10-10T06:37:24.219Z","published",null,[],"ai",[26,27,28,29],"reinforcement-learning","rlhf","robotics","ai-research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2601.18751",0,{"sections":36},[37,41,45,50,55,59,63,68,73,78,82,87,92,97],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",6834,"2026-10-09T11:52:17.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":18},"Security","security",937,{"name":46,"slug":47,"count":48,"latest_published_at":49},"Policy","policy",487,"2026-10-09T11:39:54.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Deals","deals",483,"2026-10-09T11:20:39.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":18},"Hardware","hardware",232,{"name":60,"slug":61,"count":62,"latest_published_at":18},"Science","science",194,{"name":64,"slug":65,"count":66,"latest_published_at":67},"Consumer Tech","consumer-tech",181,"2026-10-08T23:26:35.000Z",{"name":69,"slug":70,"count":71,"latest_published_at":72},"Startups","startups",117,"2026-10-08T16:45:00.000Z",{"name":74,"slug":75,"count":76,"latest_published_at":77},"Software","software",114,"2026-10-08T17:57:01.000Z",{"name":79,"slug":80,"count":81,"latest_published_at":18},"Dev Tools","dev-tools",106,{"name":83,"slug":84,"count":85,"latest_published_at":86},"General","general",66,"2026-10-09T04:46:11.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"Gaming","gaming",59,"2026-10-09T11:43:43.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",34,"2026-10-08T14:00:22.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",8,"2026-10-05T09:00:00.000Z"]