[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-fine-tuning-llms-in-the-wrong-layers-erases-skills":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},10885,"fine-tuning-llms-in-the-wrong-layers-erases-skills","Fine-Tuning LLMs in the Wrong Layers Erases Skills","Researchers show that picking the right Transformer layers to adapt, not just how you adapt them, preserves a model's general skills while it learns a new task.","A new method lets you fine-tune a language model on math or code without quietly wrecking its common sense.\n\nResearchers studied parameter-efficient fine-tuning, the standard way to teach a large language model a narrow skill without retraining the whole thing. They found that tuning different layers of a Transformer produces very different results: some layers boost the target task with minimal collateral damage, others wreck general reasoning ability. To find the good layers without expensive calculations, they used a cheap stand-in measurement, the similarity between a layer's input and output, which reliably flagged the layers most sensitive to adaptation. Adapters placed only on those layers, a method they call Layer-Selective LoRA, beat standard all-layer tuning on math and code tasks while keeping far more of the model's original commonsense reasoning intact.\n\nMost fixes for this trade-off involve replaying old training data or adding regularization terms, both of which cost extra compute or data. This result suggests a cheaper lever: just be selective about where you touch the model. For any team running frequent fine-tunes on a shared base model, that is a meaningful efficiency gain, not a marginal one.\n\nIt will not shock anyone who has watched a specialist hire struggle with basic tasks outside their lane - the fix, it turns out, is training only the parts of the brain built for change.","[\"fine-tuning\",\"lora\",\"llm\",\"ai-research\"]","2026-10-09T04:00:00.000Z","2026-10-09T20:00:37.963Z","2026-10-09T20:00:41.555Z","published",null,[],"ai",[26,27,28,29],"fine-tuning","lora","llm","ai-research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.11620",0,{"sections":36},[37,40,44,49,54,59,63,68,73,78,83,88,93,98],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",6619,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",927,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",486,"2026-10-08T22:40:11.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",474,"2026-10-08T22:00:00.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Hardware","hardware",229,"2026-10-08T20:47:10.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":18},"Science","science",192,{"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":82},"Dev Tools","dev-tools",105,"2026-10-07T16:59:11.000Z",{"name":84,"slug":85,"count":86,"latest_published_at":87},"General","general",66,"2026-10-09T04:46:11.000Z",{"name":89,"slug":90,"count":91,"latest_published_at":92},"Gaming","gaming",58,"2026-10-08T20:08:45.000Z",{"name":94,"slug":95,"count":96,"latest_published_at":97},"Reviews","reviews",34,"2026-10-08T14:00:22.000Z",{"name":99,"slug":100,"count":101,"latest_published_at":102},"How-To","how-to",8,"2026-10-05T09:00:00.000Z"]