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cs.LG2026
Entropy Centroids as Intrinsic Rewards for Test-Time Scaling
Wenshuo Zhao, Qi Zhu, Xingshan Zeng +4
An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy and Gemini Deep Think. Existi…
cs.LG2025
From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning
Yuzhen Huang, Weihao Zeng, Xingshan Zeng +2
Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology behind various large reasoning models suc…