arXiv Machine Learning October 22, 2026 By Maojiang Su, Qijie Zhu, Yu Xiao, Shuyang Yu, Minshuo Chen, Zhaoran Wang, Han Liu

Doob-Based Generative Policy Optimization

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Doob-Based Generative Policy Optimization (DoobPO) introduces a likelihood‑free framework for fine‑tuning generative policies such as diffusion and flow models. It first learns a lightweight ratio model to represent the desired policy update and then distills this ratio‑tilted target policy into the generative policy via conditional drift matching. The method supports standard policy improvement rules like PPO and GRPO, proves gradient equivalence to an ideal Doob drift matching objective, and demonstrates strong performance on MuJoCo continuous‑control benchmarks.

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