Author

L. Li

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#artificial intelligence Preprint Sep 2026

UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement

Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified capacity, we introduce UniEvo-VL, a self-evolving framework for multimodal models to learn from this constructive self-correction feedback...

Fang Wu, Dan-Lei Xing, Yan-Jie Huang et al. · 0 citations

Jul 2026

DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation

Experiments show that DenseReward outperforms general-purpose VLMs and existing robotic reward models in dense reward prediction across both simulated and real-world manipulation, and provides effective reward guidance for downstream model predictive control and reinforcement learning.

Yu Fang, Wanxi Dong, Jiaqi Liu et al. · arXiv.org · 1 citation