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物理性RSI能力究竟来自何处?关于以大语言模型作为策略的观测性缩放规律

原文标题 · Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy

作者:Zekai Li, Jiayi Yu, Jinlu Zhang | 在本文中,我们系统地研究了物理递归自我改进机制、其规模行为以及泛化能力。我们引入了PhysRSI-eval这一工具,通过五个指标来量化物理递归自我改进的效果:首次尝试的表现、效率、成功率、预算需求以及从失败中获得的改进程度。我们在固定的交互预算下,对18种不同的虚拟学习模型进行了评估。分析结果显示……

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Robotics

Title: Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy

Abstract: In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement. We introduce PhysRSI-eval to quantify physical RSI through a five-axis metric: First-Attempt Performance, Efficiency, Success, Budget and Improvement from failure. Different VLMs are evaluated on 18 manipulation tasks under a fixed interaction budget. The analysis of experimental results motivates the Physical Barrel Hypothesis: different capabilities may constrain different parts of improvement. The five-axis metrics also distinguish systems with close public benchmark averages and show that high scores need not come with early or efficient gains. We study observational scaling from two perspectives: predicting the relative performance of VLM agents from their public capabilities, and forecasting later progress from early physical feedback. Together, these analyses ask how quickly and how far an agent can improve. Moreover, we conduct workspace interventions and then analyze what improvement leaves behind. Removing history reduces late-round success, frozen workspaces remain useful on unseen layouts, and three cross-task cases achieve success earlier. The results suggest that scene-specific adaptation and reusable experience can coexist.

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