arXiv:2606.08653v1 Announce Type: cross Abstract: Action-supervised fine-tuning of vision-language-action (VLA) policies fits demonstrations effectively but constrains only the directions that change predicted actions, leaving visual structure consistent across action-equivalent states free to collapse. We formalize this as residual visual collapse along local action fibers and propose FiberTune, a training-time objective that preserves teacher-structured visual residuals without adding inference-time overhead. FiberTune uses an online action probe to estimate action-predictive feature directi
The proliferation of Vision-Language-Action (VLA) policies necessitates more robust fine-tuning methods to prevent performance degradation and improve real-world applicability.
Improving the visual perceptual stability of VLA models is crucial for reliable, safe, and effective autonomous systems, directly impacting their deployability in complex environments.
This research introduces a novel technique to preserve visual structure during VLA fine-tuning, leading to more stable and adaptable AI-driven actions without adding inference-time costs.
- · Robotics companies
- · AI researchers
- · Automation sector
- · Logistics and manufacturing
- · Developers of less robust VLA fine-tuning methods
More precise and reliable robotic actions become achievable in dynamic visual environments.
Accelerated development and adoption of sophisticated autonomous agents in various industries.
Enhanced trust and broader integration of AI-powered automation into critical infrastructure and consumer applications.
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Read at arXiv cs.LG