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

Zekai Li, Jiayi Yu, Jinlu Zhang, Luyang Niu, Zekun Qi, Jun Zheng, Xuchuan Chen, Dairu Liu, Yintianrun Zhang, He Wang, Li Yi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26848 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

在这篇论文中,我们系统地研究了物理回归自我改进的机制、扩展行为和一般化。我们介绍了PhysRSI-eval来量化物理RSI通过五轴指数:第一次尝试性能、效率、成功、预算和改进,而不是失败。不同的VLM在固定互动预算下的18项操纵任务上进行评估。实验结果的分析激励了物理桶假设:不同的能力可能会限制改进的不同部分。五轴指数也区分了有近公共基准平均值的系统,并表明高分不需要带有早期或高效的收益。我们从两个角度研究观察规模化:预测VLM代理的相对性能,从他们的宣传能力出发,并从早期物理反馈预测进展。一起,这些指标分析了如何

Machine translation (Meta M2M100); the English original follows.

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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Topics: Benchmark (surveying), Observational study, Workspace, Computer science, Scaling, Generalization

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