[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-overtraining":3},{"tag":4,"articles":9},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":8},"81653b88-e7b6-4a60-9d96-27fc360863c1","overtraining",0,null,[10],{"id":11,"slug":12,"title":13,"summary":14,"category":15,"image_url":16,"cover_image":16,"language":17,"created_at":18},"04d71e47-d4ad-45bf-b678-5bcbdb1de0ee","shannon-scaling-law-llm-overtraining-zh","香農尺度律解釋 LLM 過訓練","這篇論文把 LLM 訓練看成帶雜訊的資訊傳輸，說明為何算力增加時，模型在噪聲下反而可能變差。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779689757133-oarp.png","zh","2026-05-25T06:15:31.356036+00:00"]