@Phoenixyin13: This is one of the most important reposts I've made. The first author of this paper is someone I deeply admire and a good friend of mine—Guowei Xu, a top student from the Yao Class at @Tsinghua_Uni, who is now conducting AI large model research at @Harvard. Guowei's paper precisely hits the current...
Summary
Reposting an introduction to a paper by Tsinghua Yao Class graduate Guowei Xu (currently at Harvard) that accurately points out two critical bottlenecks in LLM search: sparse verification and candidate limitation, which are important for improving reasoning capabilities.
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Cached at: 05/29/26, 07:59 AM
This is one of my most important retweets.
The first author of this paper is someone I deeply admire and a good friend of mine, Guowei Xu, a top talent from the Yao Class at @Tsinghua_Uni. He is now conducting AI large model research at @Harvard.
Guowei’s paper precisely hits two critical bottlenecks in current LLM search:
- Sparse verification that only checks the correctness of the last step
- https://t.co/zzUqckORQv
Guowei Xu (@Kevin_GuoweiXu): 🚀 How should LLMs sample on hard reasoning problems during post-training and inference where direct rollouts rarely produce a correct answer?
Best-of-N (e.g., GRPO) and tree search share two limitations: 🔻 Verification signals are sparse 🔻 Candidates stay within the model’s
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