Google Research Finds Longer Chain-of-Thought Correlates Negatively With Accuracy

A significant research finding from Google challenges conventional wisdom about reasoning in language models. New analysis reveals that longer chain-of-thought sequences actually show negative correlation (-0.54) with accuracy across multiple model variants including GPT-OSS, DeepSeek-R1, and Qwen3, tested on rigorous benchmarks like AIME2024/2025 and GPQA-Diamond.

This finding has profound implications for local LLM deployment strategy. If longer reasoning chains don't improve accuracy and actually correlate with worse performance, practitioners should reconsider inference strategies that encourage token-heavy reasoning outputs. This could lead to more efficient local inference by generating shorter, more focused reasoning paths while maintaining or improving output quality.

For resource-constrained environments running models locally, this research suggests opportunities to optimize inference latency and VRAM consumption by curtailing reasoning token generation. The finding also implies that model training and fine-tuning approaches emphasizing extended chain-of-thought may need recalibration toward more concise reasoning patterns.

Source: r/LocalLLaMA · Relevance: 8/10