Process Supervision

Process supervision aims to improve the performance of large language models (LLMs) on complex, multi-step reasoning tasks by providing feedback not just on the final answer, but also on the intermediate steps of the reasoning process. Current research focuses on automating the generation of this step-by-step feedback, employing techniques like Monte Carlo Tree Search and leveraging the capabilities of pre-trained LLMs to create process reward models without extensive human annotation. This automated process supervision significantly enhances LLMs' accuracy in domains like mathematical problem-solving and code generation, offering a more efficient and scalable approach to training robust and reliable AI systems.