【第655期】推理时执行轨迹的对齐Harness设计 episode artwork

EPISODE · Jul 16, 2026 · 21 MIN

【第655期】推理时执行轨迹的对齐Harness设计

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Seventy3:借助NotebookLM的能力进行论文解读,专注人工智能、大模型、机器人算法、crypto方向,让大家跟着AI一起进步。如果你想要解读自己的论文,获得更多曝光度。请联系小助手微信:seventy3_podcast 加群。合作邮箱:zhiwudazhanjiangshi#gmail.com今天的主题是:Harnesses for Inference-Time Alignment over Execution TrajectoriesSummaryHarness 工程(harness engineering)已成为大型语言模型(LLM)智能体一种重要的推理时(inference-time)技术,其目标是通过任务分解和引导式执行提升智能体的长期表现。然而,更复杂的 harness 并不一定更优:增加任务分解的粒度或执行引导的强度,有时能够改善执行过程,但也可能降低最终任务的成功率。我们从推理时轨迹对齐(inference-time trajectory alignment)的视角研究 harness 的设计。该视角将 harness 拆解为两种机制:任务分解(task decomposition),负责将任务组织为一系列子目标;以及引导式执行(guided execution),负责在执行过程中重塑局部动作分布(local action distributions)。这种机制划分使我们能够量化工作流粒度(workflow granularity)、重试预算(retry budgets)以及引导导致的动作重加权(guidance-induced action reweighting)如何共同决定 harness 设计的性能上限。同时,它也揭示了若干具体的失效模式,包括过度分解(over-decomposition)、过度剪枝(over-pruning)以及幻觉式执行(hallucinated execution)。我们通过受控的合成实验(controlled synthetic experiments)和真实终端智能体基准测试(real terminal agent benchmarks)验证了上述理论预测。受该理论启发,我们进一步发现,一个高效的 harness 并不一定需要覆盖完整的执行流程:仅对任务的初始阶段进行结构化设计,而将后续执行交由智能体自主完成,相较于完全结构化的工作流,反而能够获得更高的任务通过率(pass rate)。原文链接:https://arxiv.org/abs/2605.21516前往小宇宙评论区与主播互动

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【第655期】推理时执行轨迹的对齐Harness设计

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