<ul><li>Reinforcement learning from human feedback (RLHF) is crucial for ensuring alignment of large language models (LLMs) with human values in the post-training phase.</li><li>Group relative policy optimization (GRPO) is an effective approach for RLHF, but efficient training remains a challenge.</li><li>Research shows that increasing reward variance in the initial policy model accelerates RLHF training.</li><li>A novel reward adjustment model, integrated into the GRPO algorithm as GRPO with reward variance increase (GRPOVI), significantly improves RLHF training efficiency.</li></ul>

Accelerating RLHF Training with Reward Variance Increase

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