[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-reasoning-models":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"86a3b40c-a0ec-4552-bb34-adde82217a0a","reasoning models","reasoning-models",3,"推理模型強調多步驟思考與可驗證的中間推理，常見於數學、程式與代理任務。這個標籤聚焦訓練方法、冷啟動、RLVR、損失設計與成本效能取捨。","Reasoning models are built to handle multi-step inference, verification, and agentic tasks such as math, coding, and interactive problem solving. This tag covers training methods, cold-start behavior, RLVR, loss design, and the cost-performance tradeoffs that shape deployment.",[12],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"8e6e5e5b-c51f-495e-a596-203fb64c71eb","tsallis-loss-reasoning-model-training-zh","Tsallis loss 讓推理模型更快脫困","這篇論文用 Tsallis q-logarithm 搭出一條損失函數光譜，想解決推理模型在冷啟動時卡住的問題。它把 RLVR 和 latent trajectory 的 log-marginal-likelihood 串成可調參的連續體。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777443006073-083j.png","zh","2026-04-29T06:09:37.277494+00:00"]