[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-random-layer-shuffling-cuts-model-params-by-up-to-75":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},9640,"random-layer-shuffling-cuts-model-params-by-up-to-75","Random Layer Shuffling Cuts Model Params by Up to 75%","Researchers built a recursive neural network that randomly picks which layer to reuse at each step, matching bigger models with 50-75% fewer parameters.","A new neural network architecture gets deeper by rolling dice on which layer to use next, and it needs far fewer parameters to do it.\n\nResearchers describe the Random Recursive Model, or RRM, in a paper posted to arXiv. Instead of stacking many unique layers or looping through one fixed layer repeatedly, RRM keeps a pool of learned layers and samples one at random, with replacement, for each step of computation on each example. That randomness lets the network reuse the same small set of parameters in a huge number of different orders. On difficult reasoning benchmarks, the approach matched or beat standard baselines while using 50 to 75 percent fewer parameters.\n\nThe appeal here is flexibility without retraining. The model can run more or fewer recursive steps at inference time, even deeper than anything it saw in training, and it supports a Monte Carlo style of inference that trades extra computation for better accuracy on the fly. For teams squeezed by memory and compute budgets, a model that gets smarter by thinking longer rather than growing bigger is a genuinely different lever to pull.\n\nIt is also, so far, a result on reasoning benchmarks rather than a drop-in replacement for the transformers running today's chatbots. Recursive and parameter-sharing architectures have circled around similar ideas for years without displacing brute-force scaling. Whether randomized layer reuse earns a place in production models, or stays a clever trick confined to papers, will depend on results outside the benchmark suite the authors chose.","[\"ai-research\",\"neural-networks\",\"model-efficiency\",\"research\"]","2026-10-02T04:00:00.000Z","2026-10-03T04:14:08.544Z","2026-10-03T04:14:14.921Z","published",null,[],"ai",[26,27,28,29],"ai-research","neural-networks","model-efficiency","research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.00541",0,{"sections":36},[37,40,44,48,53,57,61,66,71,76,81,86,91,96],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",5976,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",842,{"name":45,"slug":46,"count":47,"latest_published_at":18},"Policy","policy",438,{"name":49,"slug":50,"count":51,"latest_published_at":52},"Deals","deals",317,"2026-10-01T22:00:00.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":18},"Hardware","hardware",199,{"name":58,"slug":59,"count":60,"latest_published_at":18},"Science","science",173,{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",155,"2026-10-01T19:54:10.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Dev Tools","dev-tools",96,"2026-10-01T16:57:03.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Software","software",93,"2026-09-30T21:41:11.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Startups","startups",90,"2026-10-01T21:55:22.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Gaming","gaming",53,"2026-10-02T02:50:39.000Z",{"name":87,"slug":88,"count":89,"latest_published_at":90},"General","general",50,"2026-09-30T21:37:54.000Z",{"name":92,"slug":93,"count":94,"latest_published_at":95},"Reviews","reviews",31,"2026-09-28T14:31:34.000Z",{"name":97,"slug":98,"count":99,"latest_published_at":100},"How-To","how-to",7,"2026-10-01T09:00:00.000Z"]