<ul><li>Reasoning has become a significant focus for language models, but the progress often lacks methodological rigor and robust evaluation practices.</li><li>Current mathematical reasoning benchmarks are sensitive to various implementation choices, leading to unclear comparisons and unreported sources of variance.</li><li>A standardized evaluation framework with clear best practices and reporting standards is proposed to address these issues.</li><li>Reassessment of recent methods reveals that reinforcement learning approaches show modest improvements and are prone to overfitting, while supervised fine-tuning methods demonstrate stronger generalization.</li></ul>

A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility

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