EmbodiedSkills is presented, a skill-based agent framework that augments frozen VLA policies with world action models for consequence-aware action selection and demonstrates the applicability of consequence-aware selection across different policy backends and action representations.
Wei Wang, Wen-Qiao Zhang, Yu-Tong Lin et al.· 2 citations
Draft-Thinking is proposed, a lightweight adaptation framework that learns a compact, self-contained working derivation that directly supports the final answer in a long chain-of-thought reasoning model.
Jie Cao, Tianwei Lin, Zhen-Xuan Fan et al.· arXiv.org· 3 citations· ⚡1
EmbodiedSkills is a unified framework that treats each skill decision as an execution proposal: the runtime checks its prerequisites before execution and verifies the outcome afterward and provides a trainable and inspectable agent layer for turning low-level VLA policies into closed-loop embodied systems.
Wei Wang, Wen-Qiao Zhang, Yu-Tong Lin et al.· 1 citation
A novel Dual Self-Consistency Reinforcement Learning optimization paradigm is introduced, which utilizes Round-Trip Verification to penalize degenerate code and boost overall self-consistency in TikZ code.
Juekai Lin, Yun Zhu, Honglin Lin et al.· arXiv.org· 5 citations
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