DeepCompress：一种双奖励策略用于动态探索和压缩推理链
DeepCompress: A Dual Reward Strategy for Dynamically Exploring and Compressing Reasoning Chains
AI总结：
DeepCompress通过双奖励策略动态调整推理链长度，提升大推理模型的准确性和效率，实验显示在数学基准上表现优于基线方法。
摘要：
大型推理模型（LRMs）尽管表现出色，但存在认知效率问题，如过度思考简单问题和低估复杂问题。现有方法通过监督微调（SFT）或强化学习（RL）结合令牌长度奖励提升效率，但往往以牺牲准确性为代价。本文提出DeepCompress，一种新型框架，同时提升LRMs的准确性和效率。我们挑战传统一致偏好较短推理路径的方法，证明较长响应可包含更广泛正确解法。DeepCompress采用自适应长度奖励机制，实时根据模型能力分类问题为
英文摘要：
Large Reasoning Models (LRMs) have demonstrated impressive capabilities but suffer from cognitive inefficiencies like "overthinking" simple problems and "underthinking" complex ones. While existing methods that use supervised fine-tuning (SFT) or reinforcement learning (RL) with token-length rewards can improve efficiency, they often do so at the cost of accuracy. This paper introduces DeepCompress, a novel framework that simultaneously enhances both the accuracy and efficiency of LRMs. We challenge the prevailing approach of consistently favoring shorter reasoning paths, showing that longer responses can contain a broader range of correct solutions for difficult problems. DeepCompress employs an adaptive length reward mechanism that dynamically classifies problems as "Simple" or "Hard" in real-time based on the model's evolving capability. It encourages shorter, more efficient reasoning for "Simple" problems while promoting longer, more exploratory thought chains for "Hard" problems. This dual-reward strategy enables the model to autonomously adjust its Chain-of-Thought (CoT) length, compressing reasoning for well-mastered problems and extending it for those it finds challenging. Experimental results on challenging mathematical benchmarks show that DeepCompress consistently outperforms baseline methods, achieving superior accuracy while significantly improving token efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
