标题： 简洁提示：生成过程中通过连续简洁提示提升高效推理 显示英文标题

标题： ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation

摘要： 大型推理模型（LRMs）如DeepSeek-R1和OpenAI o1系列通过扩展生成长度的思维链（CoT）在复杂推理任务上取得了显著的性能提升。然而，一个新兴的问题是它们倾向于产生过于冗长的推理过程，导致效率问题。现有的提高效率的文献主要遵循推理前的范式，如提示和推理或微调和推理，但忽略了在推理生成过程中直接鼓励模型简洁表达的有前途的方向。为了填补这一空白，我们提出了一种名为ConciseHint的框架，该框架在推理过程的标记生成期间注入文本提示（手动设计或在简洁数据上训练），以持续鼓励推理模型简洁表达。此外，ConciseHint能够通过自适应调整提示强度来适应查询的复杂性，这确保了它不会损害模型性能。在最先进的LRMs（包括DeepSeek-R1和Qwen-3系列）上的实验表明，我们的方法可以在保持性能良好的同时有效生成简洁的推理过程。例如，我们在GSM8K基准测试中使用Qwen-3 4B实现了推理长度65%的减少率，几乎没有任何准确率损失。 显示英文摘要

摘要： Recent advancements in large reasoning models (LRMs) like DeepSeek-R1 and OpenAI o1 series have achieved notable performance enhancements on complex reasoning tasks by scaling up the generation length by Chain-of-Thought (CoT). However, an emerging issue is their inclination to produce excessively verbose reasoning processes, leading to the inefficiency problem. Existing literature on improving efficiency mainly adheres to the before-reasoning paradigms such as prompting and reasoning or fine-tuning and reasoning, but ignores the promising direction of directly encouraging the model to speak concisely by intervening during the generation of reasoning. In order to fill the blank, we propose a framework dubbed ConciseHint, which continuously encourages the reasoning model to speak concisely by injecting the textual hint (manually designed or trained on the concise data) during the token generation of the reasoning process. Besides, ConciseHint is adaptive to the complexity of the query by adaptively adjusting the hint intensity, which ensures it will not undermine model performance. Experiments on the state-of-the-art LRMs, including DeepSeek-R1 and Qwen-3 series, demonstrate that our method can effectively produce concise reasoning processes while maintaining performance well. For instance, we achieve a reduction ratio of 65\% for the reasoning length on GSM8K benchmark with Qwen-3 4B with nearly no accuracy loss.