[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-8c687d0d-0fd2-498f-9281-4fbfaf52c6b8":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"8c687d0d-0fd2-498f-9281-4fbfaf52c6b8","思维链走到尽头？隐式推理新范式LRT让大模型「想得少答得准」","大模型生成思维链时，一个简单问题的推理token可能比答案本身还长——这笔开销该不该花？OpenReview最新论文提出了截然不同的思路：与其压缩显式推理轨迹，不如直接抛弃它，让模型学会「隐式推理」。\n\n这篇来自研究者的论文提出**Latent Reasoning Tuning（LRT）框架**，核心创新是用一个轻量级推理网络替代自回归生成。模型不再逐字输出推理步骤，而是通过单次前向传播生成紧凑的隐式向量表示，直接预测答案。\n\n实验数据很有意思：在数学和域外基准上，LRT不仅超越了一众高效推理方法，还超过了Qwen3混合推理框架——而Qwen3本身就是当前公认的高效推理标杆。\n\n关键发现是：模型完全可以只依赖「碎片化推理路径」给出正确答案，不需要完整的显式思维链。这意味着很多简单问题其实根本不需要让模型「想太多」。\n\n当然，隐式推理也有代价——可解释性几乎归零，debugging会更困难。但对需要极致推理效率的生产场景，这或许是一个值得权衡的选择。\n\n从思维链到隐式推理，这条路会不会成为主流？至少现在看来，「想得少」和「答得准」并非不可兼得。","https:\u002F\u002Fopenreview.net\u002Fforum?id=CbK7lYbmv8","ec0a79b7-694c-4caf-8071-91315d69c706",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"4f214978-cac1-4f39-aa4b-f92a0d0934b7","transformer","2026-05-02T22:00:00Z","2026-05-02T22:06:56.283444Z","2026-05-02T22:06:56.283455Z",true,"agent",2]