[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-shrinking-llms-without-losing-their-minds":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":24,"persona_id":22,"persona_name":22,"section":25,"tags":26,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},2056,"shrinking-llms-without-losing-their-minds","Shrinking LLMs Without Losing Their Minds","New research maps exactly how much you can compress a domain-trained model before it stops being useful — and which training tricks slow the damage.","Researchers have published empirical scaling laws for compressing large language models down to deployment size without gutting their usefulness.\n\nThe paper, posted to arXiv, uses quantitative finance as a test domain to measure how model quality degrades under iterative structural pruning. The team compared two distillation approaches — logit-based and LoRA-based — and introduced a blended chain-of-thought supervision loss designed to stabilize training when compressing reasoning traces. Their core finding: in-domain task quality declines gradually and predictably under compression, but general-knowledge benchmarks collapse much earlier. The difference comes down to supervision format, not compression ratio.\n\nThat distinction matters for any team trying to ship a smaller, cheaper model without producing something that only knows one trick. Chain-of-thought supervision, the paper argues, actively recovers general knowledge that pruning erases — which means skimping on it trades breadth for a short-term benchmark win. The researchers also release FinHeadlineMix, a headline dataset, alongside their scaling law results and deployment recommendations.\n\nThe broader pitch here is a reusable decision framework: given a target compression ratio and a domain, you can now estimate the quality hit before you commit to the pruning schedule. Whether that promise holds outside quantitative finance is the obvious next question nobody in the paper answers.","[\"ai\",\"machine-learning\",\"llm\",\"research\"]","2026-06-24T04:00:00.000Z","2026-06-24T05:17:57.097Z","2026-06-24T05:18:04.353Z","published",null,[],"https:\u002F\u002Fcdn.xyz.onl\u002Farticle-images\u002Fshrinking-llms-without-losing-their-minds.webp","ai",[25,27,28,29],"machine-learning","llm","research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.24747",0,{"sections":36},[37,40,45,49,54,59,64,69,74,79,84,89,94,99],{"name":38,"slug":25,"count":39,"latest_published_at":18},"AI",528,{"name":41,"slug":42,"count":43,"latest_published_at":44},"Deals","deals",156,"2026-06-24T10:54:10.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":18},"Security","security",144,{"name":50,"slug":51,"count":52,"latest_published_at":53},"Policy","policy",102,"2026-06-24T07:03:03.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Consumer Tech","consumer-tech",84,"2026-06-23T21:34:53.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":63},"Hardware","hardware",71,"2026-06-23T16:50:03.000Z",{"name":65,"slug":66,"count":67,"latest_published_at":68},"Software","software",63,"2026-06-23T11:16:34.000Z",{"name":70,"slug":71,"count":72,"latest_published_at":73},"Dev Tools","dev-tools",53,"2026-06-23T18:13:40.000Z",{"name":75,"slug":76,"count":77,"latest_published_at":78},"Science","science",39,"2026-06-23T05:25:16.000Z",{"name":80,"slug":81,"count":82,"latest_published_at":83},"Gaming","gaming",32,"2026-06-22T17:00:00.000Z",{"name":85,"slug":86,"count":87,"latest_published_at":88},"General","general",27,"2026-06-24T08:50:14.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"Startups","startups",24,"2026-06-23T17:25:54.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",19,"2026-06-14T08:00:00.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]