DiG-Flow: Discrepancy-Guided Flow Matching for Robust VLA Models
Vision-Language-Action (VLA) models trained with flow matching have demonstrated impressive capabilities on robotic manipulation tasks. However, their performance often degrades under distribution shift and on complex multi-step tasks, suggesting that the learned representations may not robustly capture task-relevant semantics. We introduce DiG-Flow, a principled framework that enhances VLA robustness through geometric regularization. Our key insight is that the distributional discrepancy between observation and action embeddings provides a meaningful geometric signal: lower transport cost indicates compatible representations, while higher cost suggests potential misalignment. DiG-Flow computes a discrepancy measure between empirical distributions of observation and action embeddings, maps it to a modulation weight via a monotone function, and applies residual updates to the observation embeddings before flow matching. Crucially, this intervention operates at the representation level without modifying the flow matching path or target vector field. We provide theoretical guarantees showing that discrepancy-guided training provably decreases the training objective, and that guided inference refinement converges with contraction. Empirically, DiG-Flow integrates into existing VLA architectures with negligible overhead and consistently improves performance, with particularly pronounced gains on complex multi-step tasks and under limited training data.
692fac3b26742347f61dac38
Wanpeng Zhang
claimed_verified
2025-12-03T09:15:49.704Z
640dd700fdeaae139081f598
https://cdn-avatars.huggingface.co/v1/production/uploads/640dd700fdeaae139081f598/L986zu4-iOPFs9Y3_T5Ue.jpeg
Wanpeng Zhang
zawnpn
user
zawnpn
692fac3b26742347f61dac39
Ye Wang
692fac3b26742347f61dac3a
Hao Luo
claimed_verified
2026-01-21T10:16:03.805Z
6708cbdcf8a1d7b26732c038
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/CN1WHMPKfjQ8wmfwOe0ni.png
Hao Luo
Lightet
user
Lightet
692fac3b26742347f61dac3b
Haoqi Yuan
692fac3b26742347f61dac3c
Yicheng Feng
claimed_verified
2026-01-22T08:47:55.644Z
6655b86e607894ea80d74910
/avatars/663c0135c903c9c127fe1b8d8aaf279c.svg
yicheng feng
takenpeanut
user
takenpeanut
692fac3b26742347f61dac3d
Sipeng Zheng
692fac3b26742347f61dac3e
Qin Jin
692fac3b26742347f61dac3f
Zongqing Lu
DiG-Flow: Discrepancy-Guided Flow Matching for Robust VLA Models
Vision-Language-Action (VLA) models trained with flow matching have demonstrated impressive capabilities on robotic manipulation tasks. However, their performance often degrades under distribution shift and on complex multi-step tasks, suggesting that the learned representations may not robustly capture task-relevant semantics. We introduce DiG-Flow, a principled framework that enhances VLA robustness through geometric regularization. Our key insight is that the distributional discrepancy between observation and action embeddings provides a meaningful geometric signal: lower transport cost indicates compatible representations, while higher cost suggests potential misalignment. DiG-Flow computes a discrepancy measure between empirical distributions of observation and action embeddings, maps it to a modulation weight via a monotone function, and applies residual updates to the observation embeddings before flow matching. Crucially, this intervention operates at the representation level without modifying the flow matching path or target vector field. We provide theoretical guarantees showing that discrepancy-guided training provably decreases the training objective, and that guided inference refinement converges with contraction. Empirically, DiG-Flow integrates into existing VLA architectures with negligible overhead and consistently improves performance, with particularly pronounced gains on complex multi-step tasks and under limited training data.
DiG-Flow enhances VLA models' robustness by using geometric regularization to align observation and action embeddings, improving performance on complex tasks and with limited data.
flow matching distributional discrepancy transport cost empirical distributions observation embeddings action embeddings modulation weight monotone function residual updates contraction training objective inference refinement