TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning

The article introduces TraPO, a semi-supervised reinforcement learning framework designed to improve the reasoning capabilities of Large Language Models (LLMs). The focus is on leveraging reinforcement learning techniques with limited labeled data to enhance LLM performance. The research likely explores how to effectively combine supervised and unsupervised learning approaches within the reinforcement learning paradigm to achieve better reasoning outcomes.