3 papers
cs.LG2026
Learning with a Single Rollout via Monte Carlo Pass@k Critic
Fengdi Che, Yang Liu, Lei Yu +4
Estimating token-level advantages in reinforcement learning (RL) for language models remains challenging because scaling up episodic experience collection is expensive. The difficu…
cs.PL2025
VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally Verifiable Code
Lingfei Zeng, Fengdi Che, Xuhan Huang +4
Formal verification is the next frontier for ensuring the correctness of code generated by Large Language Models (LLMs). While methods that co-generate code and formal specificatio…
cs.LG2025
A Tutorial: An Intuitive Explanation of Offline Reinforcement Learning Theory
Fengdi Che
Offline reinforcement learning (RL) aims to optimize the return given a fixed dataset of agent trajectories without additional interactions with the environment. While algorithm de…