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cs.AI2025
MetaVLA: Unified Meta Co-training For Efficient Embodied Adaption
Chen Li, Zhantao Yang, Han Zhang +4
Vision-Language-Action (VLA) models show promise in embodied reasoning, yet remain far from true generalists-they often require task-specific fine-tuning, incur high compute costs,…
cs.RO2025
Reward Evolution with Graph-of-Thoughts: A Bi-Level Language Model Framework for Reinforcement Learning
Changwei Yao, Xinzi Liu, Chen Li +1
Designing effective reward functions remains a major challenge in reinforcement learning (RL), often requiring considerable human expertise and iterative refinement. Recent advance…
cs.AI2025
STELAR-VISION: Self-Topology-Aware Efficient Learning for Aligned Reasoning in Vision
Chen Li, Han Zhang, Zhantao Yang +4
Vision-language models (VLMs) have made significant strides in reasoning, yet they often struggle with complex multimodal tasks and tend to generate overly verbose outputs. A key l…