[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-tiny-27m-parameter-model-beats-giants-at-puzzle-reasoning":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},9218,"tiny-27m-parameter-model-beats-giants-at-puzzle-reasoning","Tiny 27M Parameter Model Beats Giants at Puzzle Reasoning","A 27 million parameter model trained on 1000 examples solved Sudoku and mazes and beat larger language models on a top AI reasoning benchmark.","A research team has built a reasoning model with just 27 million parameters that beats billion-parameter language models at logic puzzles.\n\nResearchers describe the Hierarchical Reasoning Model, a recurrent neural network inspired by how the brain splits slow, abstract planning from fast, detailed computation. It uses two interacting modules instead of one monolithic transformer, and skips chain-of-thought prompting entirely. Trained on just 1000 examples with no pretraining, HRM solved complex Sudoku puzzles and found optimal paths through large mazes with near-perfect accuracy. It also beat much larger language models that have far longer context windows on the Abstraction and Reasoning Corpus, a benchmark often cited as a proxy for general intelligence.\n\nChain-of-thought prompting is the default reasoning trick for today's LLMs, but it is expensive, slow, and falls apart when a task does not decompose cleanly into text steps. HRM suggests a narrower, task-specific architecture can match or beat that approach without mountains of training data or long inference chains, though it is unclear whether a model this specialized generalizes beyond puzzle-style tasks.\n\nSudoku and mazes are clean, well-defined problems; the real test is whether this hierarchical approach holds up on messier, real-world reasoning tasks where the optimal path is not so obvious.","[\"ai\",\"machine-learning\",\"reasoning-models\",\"benchmarks\"]","2026-10-01T04:00:00.000Z","2026-10-02T02:27:55.748Z","2026-10-02T02:27:57.316Z","published",null,[],"ai",[24,26,27,28],"machine-learning","reasoning-models","benchmarks",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.21734",0,{"sections":35},[36,39,43,47,52,57,61,66,71,75,80,85,90,95],{"name":37,"slug":24,"count":38,"latest_published_at":18},"AI",5629,{"name":40,"slug":41,"count":42,"latest_published_at":18},"Security","security",816,{"name":44,"slug":45,"count":46,"latest_published_at":18},"Policy","policy",430,{"name":48,"slug":49,"count":50,"latest_published_at":51},"Deals","deals",298,"2026-09-30T21:00:26.000Z",{"name":53,"slug":54,"count":55,"latest_published_at":56},"Hardware","hardware",196,"2026-09-30T13:00:00.000Z",{"name":58,"slug":59,"count":60,"latest_published_at":18},"Science","science",163,{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",149,"2026-09-30T22:57:11.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Dev Tools","dev-tools",93,"2026-10-01T02:30:48.000Z",{"name":72,"slug":73,"count":69,"latest_published_at":74},"Software","software","2026-09-30T21:41:11.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Startups","startups",84,"2026-09-30T20:39:09.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",51,"2026-09-30T16:24:30.000Z",{"name":86,"slug":87,"count":88,"latest_published_at":89},"General","general",50,"2026-09-30T21:37:54.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"Reviews","reviews",31,"2026-09-28T14:31:34.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]