arXiv:2505.09655v5 Announce Type: replace Abstract: Post-training LLMs with Reinforcement Learning, specifically Group Relative Policy Optimization (GRPO), has emerged as a paradigm for enhancing mathematical reasoning. However, standard GRPO relies on scalar correctness rewards that are often non-injective with respect to semantic content: distinct reasoning paths receive identical rewards. This leads to a Diversity-Quality Inconsistency, where the policy collapses into a narrow set of dominant modes while ignoring equally valid but structurally novel strategies. To bridge this gap, we propos
The paper identifies a crucial limitation in current reinforcement learning techniques for LLMs, specifically GRPO, which is essential for advancing mathematical reasoning in AI.
Improving mathematical reasoning in LLMs is critical for unlocking more advanced AI capabilities across scientific research, engineering, and complex problem-solving.
This research proposes a method to address the 'Diversity-Quality Inconsistency' in AI training, leading to more robust and diverse reasoning paths in large language models.
- · AI researchers
- · LLM developers
- · Scientific computing
- · AI-driven automation
- · AI models relying on narrow reasoning paths
- · Simple scalar reward systems
LLMs will develop more diverse and therefore more robust mathematical reasoning capabilities.
This improved reasoning will lead to breakthroughs in areas requiring complex problem-solving, such as drug discovery or material science.
The ability of AI to independently solve highly complex mathematical problems could accelerate technological progress across multiple sectors simultaneously.
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Read at arXiv cs.CL