3 papers
cs.LG2026
VIMPO: Value-Implicit Policy Optimization for LLMs
Zhewei Kang, Aosong Feng, Sergey Levine +2
Reinforcement learning with verifiable rewards has become a central tool for improving the reasoning ability of large language models, but current methods face a trade-off between…
cs.LG2026
Learning to Reason without External Rewards
Xuandong Zhao, Zhewei Kang, Aosong Feng +2
Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited by reliance on costly, domain-specifi…
cs.CL2025
Scalable Best-of-N Selection for Large Language Models via Self-Certainty
Zhewei Kang, Xuandong Zhao, Dawn Song
Best-of-N selection is a key technique for improving the reasoning performance of Large Language Models (LLMs) through increased test-time computation. Current state-of-the-art met…