This study finds that allowing large language models to "think" longer (generate more reasoning steps) can actually decrease their accuracy on certain tasks. The researchers identify several failure modes, including getting distracted by irrelevant info, overfitting to problem framing, and shifting to incorrect correlations. Longer reasoning may even make responses less safe in some cases, raising important questions about the current trajectory of LLM development.

The authors disclose affiliations with Anthropic, EPFL, University of Edinburgh, University of Texas at Austin, Constellation, Scale AI, Miniml. AI, and Meta. While this represents a broad range of organizations, the prominence of Anthropic affiliations warrants scrutiny for potential biases in model selection or interpretation of results.

This paper presents compelling evidence of a counterintuitive phenomenon: longer reasoning can sometimes hurt LLM performance. The experiments are well-designed to isolate specific failure modes, and the inclusion of both controlled and natural overthinking setups adds robustness to the findings. The exploration of alignment implications is valuable, though further investigation is needed. Despite limitations in task naturalness and model diversity, the overall findings are significant and merit further research.