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

arXiv: https://arxiv.org/abs/2609.10873 · HTML: https://mlsanity.com/paper/2609.10873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Builds on (most cited first, 7 of 7)

- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents](https://mlsanity.com/paper/2505.22954/) (2025)
- [WorldEval: World Model as Real-World Robot Policies Evaluator](https://mlsanity.com/paper/2505.19017/) (2025)
- [Towards Safe Policy Improvement for Non-Stationary MDPs](https://mlsanity.com/paper/2010.12645/) (2020)
- [Imperfect World Models are Exploitable](https://mlsanity.com/paper/2605.15960/) (2026)

## Abstract (arXiv)

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.