# Does a Learned Corrector Beat a Simple Retreat? Evidence from a Frozen VLA

arXiv: https://arxiv.org/abs/2610.06921 · HTML: https://mlsanity.com/paper/2610.06921/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Failure recovery and robustness, one of 13 directions in Vision-language-action models.
- First posted on arXiv 2 Oct 2026.

## Placed in

- Vision-language-action models / [Failure recovery and robustness](https://mlsanity.com/vla/failure-recovery-and-robustness/)

## Abstract (arXiv)

Before deploying runtime recovery for a frozen vision-language-action (VLA) policy, one must establish that an intervention improves success beyond ordinary run-to-run variation and that its complexity adds value over a simple action. We evaluate these questions on frozen π0.5 across four RoboTwin tasks. For each test seed, we pair rollouts with and without correction and include a same-seed base-policy re-run as a placebo. Seed-cluster intervals and prespecified comparison rules assess net gains against stochastic outcome changes. Across 3,888 paired episodes, the full pipeline raises success on beatallowbreak blockallowbreak hammer by +13.5,pp (95% interval [+9.4,+17.7]), with no detectable gain on the other three tasks at the deployed weight. Among failed base episodes on the responsive task, 43.2% succeed on a plain re-run, compared with 63.5% after correction; many nominal rescues therefore reflect the base policy's own variability. A fixed-time trigger and scripted return to an earlier joint configuration produce a net gain with no detected difference from the learned pipeline across two rounds, although our prespecified equivalence criterion is not met consistently. Pausing and a constant-action control do not yield comparable gains. On this benchmark, the decision to intervene depends strongly on the task, and a paired placebo plus a simple retreat baseline are needed to establish what learned correction contributes.