Researchers propose Guided Action Flow, an inference-time framework that uses a learned critic to guide the reverse-time sampling of pretrained flow-matching vision-language-action (VLA) policies without retraining the base model.
On LIBERO manipulation tasks, a single-task critic improves success rates from 68.0% to 82.0% on one seed window (14% gain) and from 82.0% to 86.0% on another (4% gain), while a multi-task critic achieves 10% improvement on validation data (46.0% to 56.0%).
The critic is trained from real success and failure rollouts and conditions on task features from the frozen language pathway, but held-out test set gains are modest at 2.5 percentage points (65.0% to 67.5%), indicating limited cross-task generalization.