Diversity-Aware Policy Optimization for Large Language Model Reasoning
This work addresses the need for more robust and diverse reasoning in LLMs, which is incremental as it builds on existing RL frameworks like R1-zero.
The paper tackles the problem of enhancing large language model reasoning by investigating the impact of diversity in reinforcement learning training, proposing a diversity-aware policy optimization method that achieves a 3.5% average improvement across four mathematical reasoning benchmarks.
The reasoning capabilities of large language models (LLMs) have advanced rapidly, particularly following the release of DeepSeek R1, which has inspired a surge of research into data quality and reinforcement learning (RL) algorithms. Despite the pivotal role diversity plays in RL, its influence on LLM reasoning remains largely underexplored. To bridge this gap, this work presents a systematic investigation into the impact of diversity in RL-based training for LLM reasoning, and proposes a novel diversity-aware policy optimization method. Across evaluations on 12 LLMs, we observe a strong positive correlation between the solution diversity and Potential at k (a novel metric quantifying an LLM's reasoning potential) in high-performing models. This finding motivates our method to explicitly promote diversity during RL training. Specifically, we design a token-level diversity and reformulate it into a practical objective, then we selectively apply it to positive samples. Integrated into the R1-zero training framework, our method achieves a 3.5 percent average improvement across four mathematical reasoning benchmarks, while generating more diverse and robust solutions.