2 papers
cs.AI2024
Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
Zhihan Liu, Hao Hu, Shenao Zhang +4
Large language models (LLMs) demonstrate impressive reasoning abilities, but translating reasoning into actions in the real world remains challenging. In particular, it remains unc…
cs.LG2024
How Can LLM Guide RL? A Value-Based Approach
Shenao Zhang, Sirui Zheng, Shuqi Ke +6
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorit…