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

Key points

• In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement.1
• Different VLMs are evaluated on 18 manipulation tasks under a fixed interaction budget.1
• The analysis of experimental results motivates the Physical Barrel Hypothesis: different capabilities may constrain different parts of improvement.1
• 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.1

Sources (2)

• [1]Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy
  Hugging Face Daily Papers · Oct 19, 12:00 AM

  We introduce PhysRSI-eval to quantify physical RSI through a five-axis metric: First-Attempt Performance, Efficiency, Success, Budget and Improvement from failure.

  In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement.

• [2]Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:59 PM · same content

Extractive summary: sentences quoted from the sources.