Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning

• URL: http://arxiv.org/abs/2505.17813v1
• Date: Fri, 23 May 2025 12:29:06 GMT
• Title: Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning
• Authors: Michael Hassid, Gabriel Synnaeve, Yossi Adi, Roy Schwartz,
• Abstract summary: Reasoning large language models (LLMs) rely on scaling test-time compute to perform complex reasoning tasks.<n>We demonstrate that shorter reasoning chains within individual questions are significantly more likely to yield correct answers.<n>We then observe that training on the shorter ones leads to better performance.
• Score: 45.807019099421225
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reasoning large language models (LLMs) heavily rely on scaling test-time compute to perform complex reasoning tasks by generating extensive "thinking" chains. While demonstrating impressive results, this approach incurs significant computational costs and inference time. In this work, we challenge the assumption that long thinking chains results in better reasoning capabilities. We first demonstrate that shorter reasoning chains within individual questions are significantly more likely to yield correct answers - up to 34.5% more accurate than the longest chain sampled for the same question. Based on these results, we suggest short-m@k, a novel reasoning LLM inference method. Our method executes k independent generations in parallel and halts computation once the first m thinking processes are done. The final answer is chosen using majority voting among these m chains. Basic short-1@k demonstrates similar or even superior performance over standard majority voting in low-compute settings - using up to 40% fewer thinking tokens. short-3@k, while slightly less efficient than short-1@k, consistently surpasses majority voting across all compute budgets, while still being substantially faster (up to 33% wall time reduction). Inspired by our results, we finetune an LLM using short, long, and randomly selected reasoning chains. We then observe that training on the shorter ones leads to better performance. Our findings suggest rethinking current methods of test-time compute in reasoning LLMs, emphasizing that longer "thinking" does not necessarily translate to improved performance and can, counter-intuitively, lead to degraded results.

Related papers

• ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization [16.51303604678232]
  Reasoning Compression ThroUgh Stepwise Trials (ReCUT) is a novel method aimed at balancing the accuracy and length of reasoning trajectory.<n> Experimental results across multiple math reasoning datasets and backbone models demonstrate that ReCUT significantly reduces reasoning lengths by approximately 30-50%.
  arXiv  Detail & Related papers  (2025-06-12T15:43:01Z)
• Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty [13.843606627539597]
  This study seeks to enhance the efficiency of large language models (LLMs) by promoting conciseness for simpler problems.<n>We manage the model's reasoning efficiency by dividing the reward function and including a novel penalty for output length.<n>Our approach has yielded impressive outcomes in benchmark evaluations across three datasets: GSM8K, MATH500, and AIME2024.
  arXiv  Detail & Related papers  (2025-06-12T07:49:24Z)
• Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt [74.35891434097053]
  Reasoning Large Language Models (RLLMs) have demonstrated impressive performance on complex tasks.<n>They often exhibit overthinking -- performing unnecessary reasoning steps even after arriving at the correct answer.<n>We present a quantitative analysis of overthinking from the perspective of self-doubt.<n>We introduce a simple and effective prompting method to reduce the model's over-reliance on input questions.
  arXiv  Detail & Related papers  (2025-05-29T14:30:02Z)
• Thinkless: LLM Learns When to Think [57.857534644932194]
  Reasoning Language Models, capable of extended chain-of-thought reasoning, have demonstrated remarkable performance on tasks requiring complex logical inference.<n>We propose Thinkless, a learnable framework that empowers an LLM to adaptively select between short-form and long-form reasoning.<n>On several benchmarks such as Minerva Algebra, MATH-500, and GSM8K, Thinkless is able to reduce the usage of long-chain thinking by 50% - 90%.
  arXiv  Detail & Related papers  (2025-05-19T17:24:16Z)
• Thinking Short and Right Over Thinking Long: Serving LLM Reasoning Efficiently and Accurately [29.018731931275138]
  Large Language Models (LLMs) can gain better capabilities by generating Chain-of-Thought reasoning to respond a given request.<n>However, when incorporating the two scaling dimensions, the system efficiency is dampened significantly for two reasons.<n>We present SART, a serving framework for efficient and accurate LLM reasoning.
  arXiv  Detail & Related papers  (2025-05-19T16:34:56Z)
• Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs [52.405085773954596]
  We find that large language models (LLMs) tend to overthink simple problems, generating unnecessarily long outputs, and underthink harder ones.<n>This indicates that models might misjudge problem difficulty and fail to calibrate their response length appropriately.<n> Experiments show that the generation length can be significantly reduced while maintaining acceptable accuracy.
  arXiv  Detail & Related papers  (2025-04-30T18:48:06Z)
• ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning [68.02825465552779]
  We present ThinkPrune, a simple yet effective method for pruning the thinking length for long-thinking LLMs.<n>We show that ThinkPrune results in a remarkable performance-length tradeoff -- on the AIME24 dataset, the reasoning length of DeepSeek-R1-Distill-Qwen-1.5B can be reduced by half with only 2% drop in performance.
  arXiv  Detail & Related papers  (2025-04-02T01:59:26Z)
• O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning [98.3430004984531]
  We propose Length-Harmonizing Fine-Tuning (O1-Pruner) to minimize reasoning overhead while maintaining accuracy.<n>Our code is coming soon at https://github.com/StarDewXXX/O1-Pruner.
  arXiv  Detail & Related papers  (2025-01-22T01:35:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.