Can Rephrasing Fix VLA Language Sensitivity Without Retraining?

Can changing an instruction make a frozen vision-language-action model more reliable without retraining? This episode examines what *Rephrase Before You Act* demonstrates, how its rewriting pipeline works, and where its generalization claims stop. Mikey Watts and Yuchen Cui investigate severe wording sensitivity and distill rollout evidence into reusable rules for instructions sent to an unchanged robot policy. Their headline improvement is 16–27 percent relative—not a gain of 16–27 percentage points. ([arxiv.org](https://arxiv.org/abs/2610.10526)) We cover three central questions: what oracle phrase searches reveal about hidden capability, whether policy-specific rules improve on generic rephrasing, and why stable behavior under paraphrases can sometimes mean the robot is ignoring language. The experiments involve simulated tabletop manipulation with pi-zero on SIMPLER's WidowX-style Bridge tasks and pi-zero-point-five on LIBERO. The key limitation is that successful instruction calibration in these environments does not establish reliable transfer to physical robots or unfamiliar task families. ([sttawm.github.io](https://sttawm.github.io/rephrase-before-you-act)) We also examine released artifacts, startup latency, semantic-preservation checks, and the comparison with action verification. Before collecting more demonstrations, should you first test whether your policy needs a better instruction?