Reproduce “Scaling by Diversified Experience for Vision-Language-Action Models” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Leiyu Wang, Zhaofengnian Wang, Xueqi Li, Luoyi Fan, Cewu Lu, Nanyang Ye
Researchers introduced SyVLA, a Vision-Language-Action (VLA) model that balances robust robotic task execution with preserved visual-language understanding. The model demonstrates an average real-world task success rate of 0.73 in-domain and 0.64 out-of-distribution, while maintaining strong performance on multi-modal benchmarks.
Key insights
- Decoupling control intentions from high-level reasoning through gradient-based masking can significantly improve action precision and reduce hesitation in VLA models.
- Integrating expert demonstrations with a fixed advantage assignment during RL effectively stabilizes policy updates and prevents catastrophic performance collapse in large VLA models.
- Balancing action competence with preserved visual-language understanding and reasoning is achievable through a combined architectural and training strategy, which enhances out-of-distribution generalization.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.