[{"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.",[12,21],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"dbcae3bd-5f14-4baf-9604-0011f7382732","tsallis-loss-reasoning-model-training-en","Tsallis loss for faster reasoning-model training","A Tsallis-loss continuum may help reasoning models escape cold-start stalls faster than RLVR, with tradeoffs between speed, noise, and stability.","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777443011556-1zy3.png","en","2026-04-29T06:09:38.777932+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":17,"image_url":26,"cover_image":26,"language":19,"created_at":27},"7a6580cb-935a-456c-a22d-45bab79f41c9","arc-prize-leaderboard-cost-performance-en","ARC Prize leaderboard shows cost still matters","ARC Prize’s leaderboard tracks how AI systems trade cost for score, and ARC-AGI-3 pushes agents into interactive tasks.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775143857511-5rjv.png","2026-04-02T15:30:39.888984+00:00"]