[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-distillation":3},{"tag":4,"articles":10},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":9},"a6bb7f9c-bf74-4e21-920b-019ddd1e2da3","distillation",3,"蒸餾是把大型模型的推理能力、排序偏好或生成行為，轉移到較小模型的訓練方法。它常用於降低推論成本、縮短延遲，並讓 SLM 在重排、生成與跨架構對齊上更實用。","Distillation transfers a larger model’s behavior—ranking preferences, generation patterns, or reasoning signals—into a smaller student model. It matters because teams use it to cut inference cost and latency while keeping SLMs useful for reranking, generation, and cross-architecture alignment.",[11,20],{"id":12,"slug":13,"title":14,"summary":15,"category":16,"image_url":17,"cover_image":17,"language":18,"created_at":19},"5abc17e1-200d-4005-90a2-ba5abc1187bb","select-to-think-slms-local-sufficiency-en","Select-to-Think: Let SLMs Re-rank Themselves","A new method lets small language models re-rank their own candidates instead of calling an LLM at inference time.","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777530657379-kuvy.png","en","2026-04-30T06:30:36.54762+00:00",{"id":21,"slug":22,"title":23,"summary":24,"category":16,"image_url":25,"cover_image":25,"language":18,"created_at":26},"2061a3d3-9d89-4722-ac8b-e359941b4573","tide-cross-architecture-diffusion-llm-distillation-en","TIDE distills diffusion LLMs across architectures","TIDE distills diffusion LLMs across architectures, adding noise-aware weighting and tokenizer-aware objectives to improve a 0.6B student.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777529449496-pbon.png","2026-04-30T06:10:34.03377+00:00"]