検証が学習を止めるとき：継続的身体エージェントのための更新受入監査
When Validation Stops Learning: Auditing Update Admission for Continual Embodied Agents
継続学習型身体エージェントのポリシー更新を、誤り制御と学習機会の両面から評価する「更新受入監査」プロトコルを提案し、範囲ベースの信頼ゲートが更新を過剰に拒否する問題を合成実験で示した。
著者: Qinzhen Ma, Ruihai Wu
分類: cs.AI
原文アブストラクト
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.