When Validation Stops Learning: Auditing Update Admission for Continual Embodied Agents

2026-09-09

AI Summary

研究通过新的审计协议解决持续学习中更新接纳问题，使用配对二项构造和历史参考提升方法提高学习机会。

Abstract

Independent evaluation can reject harmful policy updates yet also prevent useful continual learning. We argue that update admission must be assessed through both error control and retained learning opportunities at a stated interaction budget. We identify a concrete failure: a range-based confidence gate cannot certify unchanged old-task behavior within otherwise substantial budgets. A standard paired-binomial construction reduces this burden when outcome disagreements are rare. We also specify certified historical-reference promotion and a round-level missed-opportunity metric. In a constructed one-step pushing diagnostic with 32 seeds, fresh paired checks admit 31.6% of a common update stream at 2,000 episodes per stage, versus zero for the range-based gate; unconditional replay nevertheless learns better in closed-loop runs. A separate learned-dynamics stress test distinguishes model bias from feedback-selection error. The contribution is an admission-audit protocol with analytical and synthetic evidence; physical-robot and VLA validation remain open.

Problem

• Continual Learning
• Update Admission
• Embodied Agents
• Error Control
• Learning Opportunities

Innovation

• update admission
• continual learning
• confidence gate
• paired-binomial construction
• missed-opportunity metric