Video world models can produce visually convincing yet physically inconsistent sequences, raising concerns about their reliability for prediction and planning in embodied AI systems. Existing evaluations often rely on model-based judgments or reference videos, while direct physical tests largely focus on mechanics. We...
Ming-Ju Gao, Qing-Le Liu, Yu-Zhao Peng et al.· 0 citations
Coding agents can construct executable programs and revise them through feedback, offering a foundation for embodied control without target-task fine-tuning. Through a programmatic robot interface, the agent observes the scene, executes control programs, and adapts them to the resulting physical state, serving as the c...
Wen-Yi Li, Yi-Zhe Chi, You-Jie Zheng et al.· 0 citations
Active multimodal agents use visual tools to acquire task-relevant evidence while reasoning. Although reinforcement learning samples multiple interaction trajectories per input, outcome-based objectives primarily use the group to estimate scalar advantages, leaving complementary visual discoveries underused. We introdu...
Zheng Jiang, Hou-De Qian, Yi-Ming Chen et al.· 0 citations
Direct On-Policy Distillation (Direct-OPD) is proposed, which transfers the teacher's RL-induced policy shift instead of running sparse-reward RL on the target model and consistently leverages weaker teachers to improve stronger target models.
Shiyuan Feng, Huan Gao, Haohan Chi et al.· arXiv.org· 8 citations· ⚡1
Open-MOPD, a principled framework incorporating token-share balancing, gap-aware dynamic budget allocation, and student reward refresh, systematically restore cross-domain balance, elevating headroom recovery from 35.6% to 83.4% in a single deployable student.
Huan Gao, Haohan Chi, Yong Yan et al.· 12 citations· ⚡6
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
