OGER Framework Unifies Offline Guidance and Online RL for Enhanced LLM Exploration

A new framework called OGER has been introduced to address limitations in Reinforcement Learning with Verifiable Rewards (RLVR) for Large Language Models (LLMs). The approach combines offline teacher guidance with online reinforcement learning through specialized reward modeling. OGER utilizes multi-teacher collaborative training to create an auxiliary exploration reward that draws from both offline trajectories and the model's own entropy. This incentivizes autonomous exploration beyond the model's initial latent space. Extensive testing across mathematical and general reasoning benchmarks shows OGER outperforms existing baseline methods. The framework was detailed in a paper published on arXiv with identifier 2604.18530v1. It represents an advancement in addressing exploration challenges that have persisted despite previous entropy-driven strategies and offline guidance approaches. The research demonstrates improved performance in LLM reasoning tasks through this hybrid methodology.