Siyin Wang

Abstract Reinforcement learning (RL) post-training of Vision-Language-Action (VLA) models has shown strong promise for robotic manipulation. Among RL methods, critic-based approaches rely on a value estimator that predominantly operates on single-frame observations or single-frame VLM backbone latents, which is a fundamental mismatch with the partially observable nature of robot control.

📣 World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning 🤔 Current LVLMs struggle with grounding in embodied environments, how can we make AI agents understand the physical world like humans?