This work identifies an implicit advantage symmetry inherent in Group Relative Advantage Estimation (GRAE) as a structural property that provides a new perspective for understanding bottlenecks in reinforcement learning, and proposes Asymmetric GRAE (A-GRAE), which dynamically modulates exploration incentives and sampl...
Zhiqiu Yu, Zhangquan Chen, Meng-Ting Liu et al.· arXiv.org· 6 citations
Flow-matching policies are promising for imitation learning because they model complex multimodal action distributions. However, their stochasticity is largely passive: repeated sampling may yield diverse behaviors, but users cannot directly choose among valid continuations from the same state. We propose Source-Lifted...
He Zhang, Ying Sun, Pengteng Li et al.· 0 citations
Five representative interfaces are implemented -- discrete state prompt, VLM prefix, action prefix, state expert, and feature modulation -- under matched implementation details, and evaluated on 45 atomic tasks spanning three task families plus 20 composite tasks.
Yiren Zhao, Ziyang Chen, Zi-Yang Rao et al.· 0 citations
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