Driving world action models (WAMs) learn from paired trajectories and future observations. Post-training with reinforcement learning allows the policy to explore alternative trajectories and improve planning using driving rewards. However, driving logs contain future videos only for the recorded trajectories, leaving t...
Chang-Hui Jing, Hao-Chen Tian, Hong-Chen Li et al.· 0 citations
This work studies a lightweight coding harness whose execution loop is fixed while three components are varied: planning, action space, and context management, and finds that context management becomes increasingly valuable as the context-window budget tightens.
Run-Ze Fan, Zihao Zhang, Si-Min Ma et al.· 6 citations· ⚡1
Building effective AI systems increasingly depends on writing high-quality task requirements, yet users often struggle to articulate the constraints, preferences, and edge cases that determine success. This problem is especially acute in AI development, where behavior is shaped not only by human expectations but also b...
Pengshan Cai, Zi-Hao Zhang, Ting Jin et al.· 0 citations
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