Reproduce “BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning” on OpenResearch

About this paper

Han Zhong, Yutong Yin, Shenao Zhang, Xiaojun Xu, Yuanxin Liu, Yifei Zuo, Zhihan Liu, Boyi Liu and 5 more

BRiTE introduces a framework that leverages reinforcement learning to automatically generate high-quality thinking processes for Large Language Models. This approach significantly improves LLM reasoning capabilities in tasks like mathematics and coding, often matching or exceeding performance achieved with expensive human-annotated data.

Key insights

• Complex LLM reasoning can be formalized within a unified probabilistic graphical model that explicitly includes latent thinking processes (Chain-of-Thought) and evaluation signals.
• Reinforcement learning, particularly with a novel reward shaping mechanism that aligns with autoregressive token generation, can effectively bootstrap and generate high-quality reasoning rationales automatically.
• The proposed framework unifies existing LLM training paradigms like Supervised Fine-tuning, Reinforcement Learning from Human Feedback (including DPO), and rejection sampling, providing a common theoretical foundation and convergence guarantees.

Summary by alphaXiv.