[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-scaling-law":3},{"tag":4,"articles":10},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":9},"50b0671c-5172-4f21-9910-87cd91844c9b","scaling law","scaling-law",0,null,[11],{"id":12,"slug":13,"title":14,"summary":15,"category":16,"image_url":17,"cover_image":17,"language":18,"created_at":19},"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"]