AI Research Analysis
Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning
Authored by Michael Hassid, Gabriel Synnaeve, Yossi Adi, Roy Schwartz
Affiliations: FAIR Team, Meta; The Hebrew University of Jerusalem
Executive Impact: Transforming LLM Reasoning Efficiency
This research challenges the conventional wisdom that longer thinking chains improve LLM reasoning, demonstrating that shorter chains are often more accurate and computationally efficient. We introduce a novel inference method, short-m@k, that prioritizes brevity to boost performance and reduce costs significantly.
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Accuracy Gain (Shortest vs. Longest Chains)
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Compute Reduction (Shortest vs. Random)
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Max Accuracy with Shorter Chains
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Wall Time Reduction (short-3@k)
Deep Analysis & Enterprise Applications
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Initial observations from the research indicate a surprising inverse relationship between thinking chain length and correctness. Across multiple leading LLMs and complex math benchmarks, we found that selecting the shortest reasoning chain within individual questions consistently yields more accurate answers. This challenges the conventional wisdom that more 'thinking' always leads to better results, suggesting efficiency can also mean higher quality.
34.5%
Average Accuracy Gain (Shortest vs. Longest Chains)
To operationalize the 'shorter is better' principle, we introduce short-m@k, a novel LLM inference method. This approach executes multiple generations in parallel but crucially terminates computation as soon as the 'm' shortest thinking processes are completed. The final answer is then determined by majority voting among these m chains, with ties broken by selecting the shortest answer. This dramatically reduces computational cost and inference time while boosting performance.
Enterprise Process Flow: short-m@k Inference
Execute k parallel generations
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Halt computation when m shortest finish
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Select answers from m shortest chains
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Majority vote for final answer
Further validating our findings, we finetuned an LLM using datasets specifically curated with short, long, and randomly sampled reasoning chains. The results clearly show that training on shorter reasoning trajectories not only leads to models that generate shorter outputs at inference but also significantly improves overall model performance. This indicates that optimizing for brevity can be embedded directly into the LLM's training paradigm for sustained benefits.
S1-Short Finetuning Outperforms Longer Chains
Experiments finetuning the Qwen-2.5-32B model on S1-short, S1-long, and S1-random datasets revealed that training on shorter reasoning trajectories (S1-short) not only yields shorter thinking lengths but also improves model performance by 2.8% (average accuracy over S1-random).
Conversely, finetuning on longer chains (S1-long) consumed more tokens with no significant performance gains, highlighting the diminishing returns of extended 'thinking' during training. This approach offers a pathway to developing more efficient and high-performing reasoning LLMs from the ground up.
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Estimated Annual Cost Savings
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Estimated Annual Hours Reclaimed
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Your Journey to Efficient LLM Reasoning
A typical roadmap for integrating and optimizing advanced LLM reasoning within your enterprise, focusing on speed and accuracy.
Phase 1: Discovery & Strategy
Assess current LLM usage, identify key reasoning bottlenecks, and define clear objectives for efficiency and accuracy improvements. Develop a tailored strategy for leveraging shorter thinking chains and `short-m@k`.
Phase 2: Pilot Implementation & Benchmarking
Implement `short-m@k` on a pilot project, deploying and testing across critical reasoning tasks. Rigorously benchmark performance against traditional methods to quantify improvements in accuracy, compute, and latency.
Phase 3: Custom Finetuning & Optimization
Based on pilot results, curate specific datasets for finetuning existing LLMs on shorter, more accurate reasoning trajectories. Optimize models for specific enterprise use cases, ensuring maximal efficiency and performance gains.
Phase 4: Full-Scale Deployment & Monitoring
Roll out optimized LLM reasoning across relevant enterprise functions. Establish continuous monitoring systems for performance, cost, and user satisfaction, iterating for ongoing improvements and expanding capabilities.
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