飞行中的思考：通过潜在思维策略优化提升测试时推理
Thinking on the Fly: Test-Time Reasoning Enhancement via Latent Thought Policy Optimization
AI总结：
LTPO通过在测试时优化潜在思维向量，提升LLM在复杂推理任务中的鲁棒性和准确性。
摘要：
近年来，大型语言模型（LLMs）的进步使推理方式从显式的链式思维（CoT）转向更高效的潜在推理，其中中间的思维过程被表示为向量而非文本。然而，潜在推理在面对具有挑战性和分布外任务时可能变得脆弱，而这些任务对鲁棒推理最为关键。为克服这些限制，我们引入了潜在思维策略优化（LTPO），这是一种参数无关的框架，能够在测试时完全增强LLM的推理能力，而无需更新模型参数。LTPO将中间的潜在
英文摘要：
Recent advancements in Large Language Models (LLMs) have shifted from explicit Chain-of-Thought (CoT) reasoning to more efficient latent reasoning, where intermediate thoughts are represented as vectors rather than text. However, latent reasoning can be brittle on challenging, out-of-distribution tasks where robust reasoning is most critical. To overcome these limitations, we introduce Latent Thought Policy Optimization (LTPO), a parameter-free framework that enhances LLM reasoning entirely at test time, without requiring model parameter updates. LTPO treats intermediate latent "thought" vectors as dynamic parameters that are actively optimized for each problem instance. It employs an online policy gradient method guided by an intrinsic, confidence-based reward signal computed directly from the frozen LLM's own output distributions, eliminating the need for external supervision or expensive text generation during optimization. Extensive experiments on five reasoning benchmarks show that LTPO not only matches or surpasses strong baselines on standard tasks but also demonstrates remarkable robustness where others fail. Most notably, on highly challenging AIME benchmarks where existing latent reasoning baselines collapse to near-zero accuracy, LTPO delivers substantial improvements, showcasing a unique capability for complex reasoning.
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。
