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

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Critical study of LM reasoning benchmarks: pitfalls and reproducible evaluation

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

This paper presents a critical empirical study of language model reasoning benchmarks, revealing high sensitivity to implementation choices like decoding parameters, random seeds, prompt formatting, and hardware/software configurations. The authors propose a standardized evaluation framework with best practices and reporting standards. Using this framework, they reassess recent methods and find that most reinforcement learning (RL) approaches yield only modest improvements—far below prior claims—and are prone to overfitting, especially on small-scale benchmarks like AIME'24. In contrast, supervised finetuning (SFT) methods show consistently stronger generalization. The paper releases all code, prompts, and model outputs to foster reproducibility.

Key Features

Comprehensive empirical study of language model reasoning benchmarks

Identifies high sensitivity to decoding parameters, random seeds, prompt formatting, and hardware/software configurations

Proposes a standardized evaluation framework with best practices and reporting standards

Reassessment of recent RL and SFT methods on mathematical reasoning benchmarks

Release of code, prompts, and model outputs for reproducibility

Pros & Cons

Pros

Provides rigorous methodological analysis of reasoning benchmarks
Releases all code, prompts, and model outputs to enable reproducibility
Proposes clear best practices and reporting standards for future work
Identifies overfitting and modest gains in popular RL methods

Cons

Focus is limited to mathematical reasoning benchmarks (e.g., AIME'24)
Not a standalone tool or API—requires reproducing experiments locally
Findings may not generalize to all reasoning domains or model architectures

Best For

Academic research on language model reasoning capabilitiesBenchmark design and evaluation methodology for AI reasoning tasksReproducibility auditing of published reasoning resultsComparison of RL and SFT approaches for mathematical reasoning

FAQ

What does this paper find about reinforcement learning (RL) methods for reasoning?

Most RL approaches yield only modest improvements over baselines—far below prior claims—and are prone to overfitting, especially on small-scale benchmarks like AIME'24.

What is the proposed evaluation framework?

The paper proposes a standardized evaluation framework with clearly defined best practices and reporting standards to address the sensitivity of reasoning benchmarks to implementation choices.

Where can I access the code and data?

The authors release all code, prompts, and model outputs to foster reproducibility on arXiv.