[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-fine-tuning-moe-models-just-got-much-cheaper":10,"sections":34},{"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":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},3497,"fine-tuning-moe-models-just-got-much-cheaper","Fine-tuning MoE Models Just Got Much Cheaper","A new framework called EPnG cuts the parameters needed to fine-tune mixture-of-experts models by up to 180x without sacrificing accuracy.","A research method called EPnG can fine-tune large mixture-of-experts AI models by updating less than 1% of their parameters.\n\nMixture-of-experts (MoE) models route different inputs to different specialist sub-networks, which makes them efficient to run but awkward to adapt. Standard fine-tuning tools like LoRA treat all experts equally, wasting compute on rarely-used ones. EPnG takes a different approach: it reads the router's own probability scores to figure out which experts are pulling weight, prunes the idle ones, and redistributes that saved capacity to the busy ones using a technique called rank growth with orthogonal initialization. The total parameter budget stays fixed throughout. Tested on OLMoE and Qwen1.5-MoE, EPnG matched full fine-tuning performance while touching only 0.55% to 0.72% of parameters — 140x to 180x fewer than a full update.\n\nThis matters because MoE architectures are increasingly the default for frontier models — Mistral, Google, and others have shipped MoE-based systems — yet the tooling for adapting them has lagged behind. If EPnG's gains hold outside the paper's benchmarks, organizations that can't afford to fully retrain a large MoE model gain a credible path to customization. The router-aware approach also surfaces a broader insight: fine-tuning tools designed for dense models are a poor fit for sparse architectures.\n\nThe results are compelling on paper, but academic fine-tuning benchmarks rarely capture the messiness of real deployment data — so treat the 140x-180x figure as a ceiling until someone stress-tests it in production.","[\"ai\",\"machine-learning\",\"fine-tuning\",\"mixture-of-experts\"]","2026-07-03T04:00:00.000Z","2026-07-03T07:16:38.011Z","2026-07-03T07:16:40.984Z","published",null,[],"ai",[24,26,27,28],"machine-learning","fine-tuning","mixture-of-experts",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.01789",0,{"sections":35},[36,40,45,50,55,60,65,70,75,80,85,89,94,99],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":86,"slug":87,"count":83,"latest_published_at":88},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]