Are We Measuring Strategy or Phrasing? The Gap Between Surface- and Approach-Level Diversity in LLM Math Reasoning
arXiv:2606.29985v1 Announce Type: new Abstract: Diversity in LLM mathematical reasoning is critical for exploration, but common diversity metrics mostly capture surface-level variation rather than differences in how a problem is solved. We address this gap by introducing approach-level diversity: variation in strategies across correct solutions to the same problem. Using a human-calibrated LLM judge framework, we show that prior diversity measures are unreliable proxies for approach-level diversity, and this mismatch carries over to diversity-aware RLVR, where target metrics are preserved whil
The rapid advancement of LLMs necessitates more sophisticated evaluation methods to truly understand their capabilities beyond surface-level performance.
Accurate assessment of LLM reasoning strategies, rather than just output phrasing, is critical for developing more robust and reliable AI systems, especially in complex domains like mathematics.
This research introduces a method for distinguishing between surface-level and approach-level diversity in LLM mathematical reasoning, offering a more nuanced understanding of AI learning and problem-solving.
- · AI researchers
- · LLM developers
- · companies deploying LLMs in critical applications
- · Developers relying solely on surface-level diversity metrics
Improved metrics will lead to more effective training and fine-tuning of LLMs for complex, strategic reasoning tasks.
Better understanding of LLM reasoning will enable the development of AI systems capable of more human-like, adaptive problem-solving across various domains.
This could accelerate the deployment of high-autonomy AI agents that require robust and verifiable strategic reasoning, reducing a barrier to adoption.
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Read at arXiv cs.CL