Value-Rectified Distillation for Flow-based Offline Reinforcement Learning
Value-Rectified Distillation is proposed, a novel framework that reformulates offline policy distillation as a value-guided generative trajectory alignment problem and improves existing generative baselines across diverse continuous control and planning tasks on the D4RL, OGBench, and Minari benchmarks, especially over...
Ke Jiang, Wen Jiang, Yoshinobu Kawahara et al.
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