AI RESEARCH
Positive-Only Drifting Policy Optimization
arXiv CS.LG
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ArXi:2604.16519v1 Announce Type: new 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