VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction

Anticipate Before Acting: Future-State-Conditioned Vision-Language Navigation

Future-State-Conditioned VLN (FSC-VLN), a deployable model that augments a causal policy with a future-query token and uses training-only future-state supervision to distill information from future observations into the policy state, is proposed.

Decoding Task Progress from VLA Representations

The results suggest that VLAs have rich, linearly readable internal representations of semantic quantities like task progress, and that learning to read these signals offers a lightweight, interpretable path toward monitoring deployed visuomotor policies.

PHR-VLA: Planning Horizon Reasoning for Vision-Language-Action Models

PHR-VLA introduces a lightweight auxiliary future head that, during training, aligns the VLA's internal representations with latent dynamics extracted from future observations, demonstrating that privileged latent dynamics alignment provides an effective training signal for improving anticipatory reasoning in VLA policies.

RA-VLA: Retrieval-Augmented VLA for Test-Time Adaptation

RA-VLA is presented, a retrieval-augmented VLA framework that integrates behavior-aligned context retrieval with a grounded execution pipeline that facilitates seamless task adaptation while preserving inference efficiency.

Constrained Multi-Objective Post-Training for Vision-Language-Action Models

StellaVLA: In-Context Structured Demonstration for Generalizable Vision-Language-Action Models

The real-robot benchmark demonstrates that StellaVLA can use both human/robot demos and human-to-robot (XR) demos as in-context structured demonstration to help VLA model adapt to OOD tasks.