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Ant Group, Zhejiang University, Shanghai Innovation Institute, Westlake University Model AWorld-RL/RODS
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RODS synthesizes new training data on-the-fly, enabling agents to maintain high performance with 20x fewer trajectories than traditional methods.
Achieve 2.6x faster autoregressive world model inference without retraining by caching and selectively reusing block-level residuals across generation chunks.