Trust-Calibrated VLA Policy Post-Training with Evolving Imagination
Vision-Language-Action (VLA) models are often pretrained via imitation learning, making reinforcement learning (RL) post-training important when deployment moves beyond demonstration coverage. Existing refinement paradigms expose a reliability--efficiency tension: model-free refinement avoids simulator bias but is interaction-intensive, whereas model-based refinement can exploit unreliable imagination even as its simulator adapts. We propose Model-Evolving Reinforcement Learning (MERL), a trust-calibrated framework that couples policy optimization with an online-updated simulator comprising a world model and a reward proxy head. Across stages, simulator reliability controls the imagined-d...
| Authors | Wenzhan Li, Yiran Qin, Heng Zhou et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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