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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

Japanese

この論文では、我々は体系的に物理的回復自己改善のメカニズム、スケーリング行動、および一般化を調査します。我々は、物理的RSIを5軸メトリックを通じて量子化するためのPhysRSI-evalを導入します:最初の試みのパフォーマンス、効率性、成功、予算および改善の失敗からのメカニズム。異なるVLMは、固定相互作用予算の下で18の操作タスクで評価されています。実験結果の分析は、物理バレル仮説を動機づけています:異なる能力は改善の異なる部分を制限することができます。5軸メトリックはまた、近い公的ベンチマーク平均を有するシステムを区別し、高得点の必要性が早期

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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