[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-which-training-data-matters-depends-on-the-model-you-use":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},9603,"which-training-data-matters-depends-on-the-model-you-use","Which Training Data Matters Depends on the Model You Use","A new study finds the best training-data selection strategy shifts with network width and architecture, undercutting one-size-fits-all shortcuts.","Picking the best training data for a model isn't a fixed rulebook. It depends on which model you're training.\n\nResearchers studying coreset selection, the practice of choosing a smaller, budget-limited subset of training samples, compared two common strategies: picking the easiest examples first versus picking examples that give broad geometric coverage of the data. Depending on the budget size, one strategy beats the other, with a crossover point separating the two regimes. By freezing the exact same selected subsets and only swapping the learner, the team found that doubling the width of a ResNet-18 model moved that crossover point from 57 to 85 samples per class on a downsized ImageNet-100 test. Changing image resolution, adjusting stride settings, or replacing the convolutional network with a Vision Transformer also shifted or erased the boundary, and coverage-based selection won across the board once a ViT was used, even when the easy-example subsets came from a convolutional model.\n\nThat is a problem for a popular efficiency shortcut: pick a smaller training set once, then reuse it across different models to save on compute. This research suggests that shortcut can quietly break the moment you change a model's width or swap architectures, because the subset that works best for one network is not necessarily best for another.\n\nSo before trimming a dataset to cut training costs, check whether you are also changing models. The researchers are upfront that they have not found a universal scaling law, just evidence that what counts as the best data is a moving target.","[\"coreset-selection\",\"machine-learning\",\"neural-network-architecture\",\"ai-research\"]","2026-10-02T04:00:00.000Z","2026-10-03T02:32:09.267Z","2026-10-03T02:32:14.024Z","published",null,[],"ai",[26,27,28,29],"coreset-selection","machine-learning","neural-network-architecture","ai-research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.00221",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",5896,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",837,{"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",171,{"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"]