English(EN) How Much Can Best-of-N Help a World Action Model? Measuring Selectable Headroom and Realizing It in Closed Loop

新方法可准确衡量 AI 模型选择潜力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种新方法，可以准确衡量“最佳 N 项”（best-of-N）选择策略为世界动作模型（WAMs）提供的潜在改进。该方法称为去噪反事实协议（denoised counterfactual protocol），通过可复现的分支和多重续写来避免高估可选余量。研究发现，标准估计值常常夸大了实际收益，对于三个 WAMs，实际可选份额在 24% 到 66% 之间。研究还表明，结构化动作扰动可以创造额外的可选余量，并且想象中的未来包含有价值的信息，而当前模型（如 Cosmos Policy）并未充分利用这些信息。 AI

影响为评估和改进复杂动作模型中的 AI 决策提供了一种更准确的方法。

排序理由详细介绍新方法论和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Haojia Gao, Yue Cao, Yumin Liu, Haohua Que, Mingkai Liu, Guanqiu Guo, Zhuo Zou, Bokui Chen, Xueqian Wang · 2026-10-28 04:00

Best-of-N 如何帮助世界级动作模型？测量可选余量并在闭环中实现它

arXiv:2610.37196v1 Announce Type: cross Abstract: World action models (WAMs) predict the visual consequences of the actions they generate, which makes test-time selection a natural way to improve a fixed policy: imagine the futures of candidate actions and execute the best one. T…

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Best-of-N 如何帮助世界级动作模型？测量可选余量并在闭环中实现它

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