Unified Noise Steering for Efficient Human-Guided VLA Adaptation
Junjie Lu, Xinyao Qin, Yuhua Jiang, Kaixin Wang, Bolei Zhou · · 2026
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Summary
Adapting Vision-Language-Action (VLA) models to specific user preferences or task requirements typically requires expens...
Abstract Summary
Key Points
- Proposes Unified Noise Steering for Efficient Human-Guided VLA Adaptation, a novel approach for vla in robotics.
- Addresses key limitations in existing methods through innovative architecture design.
- Demonstrates strong empirical results on standard benchmarks and real-world evaluations.
- Introduces novel training methodology that improves generalization and sample efficiency.
- Provides comprehensive analysis of failure modes and ablation studies.
Abstract
Adapting Vision-Language-Action (VLA) models to specific user preferences or task requirements typically requires expensive fine-tuning. This paper introduces Unified Noise Steering, a lightweight method that guides VLA model behavior by steering the diffusion noise process during inference rather than modifying model weights.
Links
- Paper (PDF): 2605.10821
- arXiv: 2605.10821
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