Positive-Only Drifting Policy Optimization

Qi Zhang

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arXiv · 2604.16519

Positive-Only Drifting Policy Optimization

Qi Zhang

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Abstract

In the field of online reinforcement learning (RL), traditional Gaussian policies and flow-based methods are often constrained by their unimodal expressiveness, complex gradient clipping, or stringent trust-region requirements. Moreover, they all rely on post-hoc penalization of negative samples to correct erroneous actions. This paper introduces Positive-Only Drifting Policy Optimization (PODPO), a likelihood-free and gradient-clipping-free generative approach for online RL. By leveraging the drifting model, PODPO performs policy updates via advantage-weighted local contrastive drifting. Relying solely on positive-advantage samples, it elegantly steers actions toward high-return regions while exploiting the inherent local smoothness of the generative model to enable proactive error prevention. In doing so, PODPO opens a promising new pathway for generative policy learning in online settings.

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Qi Zhang. 2026-04-15. Positive-Only Drifting Policy Optimization. https://arxiv.org/abs/2604.16519

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