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

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.

Artificial Intelligence (cs.AI)

arXiv:2609.10873 [cs.AI]

(or arXiv:2609.10873v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.10873 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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