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

TL;DR;

The article discusses the challenges in evaluating math reasoning models, highlighting the lack of transparency and statistical grounding in benchmarking practices. It proposes a standardized evaluation framework and re-evaluates existing models, finding that reinforcement learning approaches yield only modest improvements compared to supervised finetuning methods.