Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning

A new approach called Supervised Reinforcement Learning (SRL) is being proposed to tackle the challenges faced by Large Language Models (LLMs) in multi-step reasoning tasks. Traditional methods like Reinforcement Learning with Verifiable Rewards often fall short when correct solutions are infrequent, and Supervised Fine-Tuning can lead to overfitting. SRL aims to bridge this gap, potentially enhancing the performance of LLMs in complex reasoning scenarios. This development is significant as it could lead to more effective AI systems capable of handling intricate tasks, making them more useful in real-world applications.