[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-cherry-squeezes-a-1b-model-into-227m-parameters":10,"sections":41},{"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":30,"tags":31,"sources":36,"feedback":40,"feedback_at":22,"cost_usd":40,"total_tokens":40},3116,"cherry-squeezes-a-1b-model-into-227m-parameters","CHERRY Squeezes a 1B Model Into 227M Parameters","A new training recipe cuts a billion-parameter transformer down to 227M while keeping loss figures close to a model twice its size.","A research team has published CHERRY, a three-part method for training smaller language models without proportionally smaller results.\n\nThe core trick is depth compression: a 48-layer, 1B-parameter transformer gets collapsed to 6 layers (227M parameters) by averaging adjacent layers, then restored through learned recurrent unrolling. With 34 effective recurrent layers, the compressed model reaches a held-out loss of 2.934 — close to, but not quite matching, a 566M dense model at 2.926, representing a 2.5x parameter reduction with a narrow gap in quality. Stacking several of these compressed models as a Mixture of Efficient Experts further closes the gap: a 2-expert setup hits 2.789, beating any single compressed model at comparable active parameters. A third technique, Selective Ground Truth Token Training, concentrates supervision on roughly 15% of tokens carrying semantic payload and claims 4.5x per-supervised-token efficiency — though the authors note that effect depends on natural-language structure and collapses on shuffled text.\n\nEfficiency research has grown into its own arms race as inference costs keep climbing, and CHERRY's recurrent recovery angle is a different bet than the pruning and quantization approaches most labs lean on. Fitting competitive performance into a 227M footprint matters most at the edge, where memory and power budgets are fixed.\n\nThe team validates everything on CHERRY-1.8B, a Korean foundation model, and is upfront that their evidence covers one model family on one language using loss-based metrics only — a candor that is rarer than it should be in this field.","[\"language models\",\"efficiency\",\"model compression\",\"ai research\"]","2026-07-01T04:00:00.000Z","2026-07-01T07:36:15.562Z","2026-07-01T07:36:18.469Z","published",null,[24],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"The body states the recurrent model 'matches a 566M dense model on held-out loss' but the source shows the compressed model reached 2.934 versus the 566M dense model's 2.926 — those are close but not matching, and the article's phrasing overstates the result; correct the comparison to reflect the actual figures.","resolved","ai",[32,33,34,35],"language models","efficiency","model compression","ai research",[37],{"name":38,"url":39},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.31796",0,{"sections":42},[43,47,52,57,62,67,72,77,82,87,92,96,101,106],{"name":44,"slug":30,"count":45,"latest_published_at":46},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":48,"slug":49,"count":50,"latest_published_at":51},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":53,"slug":54,"count":55,"latest_published_at":56},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":58,"slug":59,"count":60,"latest_published_at":61},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":63,"slug":64,"count":65,"latest_published_at":66},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":68,"slug":69,"count":70,"latest_published_at":71},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":73,"slug":74,"count":75,"latest_published_at":76},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":78,"slug":79,"count":80,"latest_published_at":81},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":93,"slug":94,"count":90,"latest_published_at":95},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":97,"slug":98,"count":99,"latest_published_at":100},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":102,"slug":103,"count":104,"latest_published_at":105},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":107,"slug":108,"count":109,"latest_published_at":110},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]