A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1
This work addresses the challenge of enhancing reasoning in AI models for applications requiring complex problem-solving, though it is incremental as it builds on existing reinforcement learning techniques.
The paper tackles the problem of improving large language models' reasoning capabilities by transitioning from conventional autoregressive methods to a deliberate, step-by-step reasoning approach, as demonstrated by OpenAI o1's use of reinforcement learning to integrate reasoning steps during inference, resulting in significant improvements.
OpenAI o1 has shown that applying reinforcement learning to integrate reasoning steps directly during inference can significantly improve a model's reasoning capabilities. This result is exciting as the field transitions from the conventional autoregressive method of generating answers to a more deliberate approach that models the slow-thinking process through step-by-step reasoning training. Reinforcement learning plays a key role in both the model's training and decoding processes. In this article, we present a comprehensive formulation of reasoning problems and investigate the use of both model-based and model-free approaches to better support this slow-thinking framework.