This post discusses the application of Offline Reinforcement Learning (RL) in enhancing the reasoning capabilities of Large Language Models (LLMs) through multi-step reasoning. The focus is on using RL techniques in situations where interactions with the environment are limited or already collected data is utilized rather than real-time learning. This approach poses both conceptual challenges and opportunities for improving model performance without requiring extensive online feedback systems. The comments highlight some confusion around the complexities of RL in this context, indicating a need for more straightforward explanations and perhaps visual aids to demystify the process.