Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models

arXiv:2605.23522v1 Announce Type: new Abstract: Reinforcement learning (RL) has become an effective way to improve prompt alignment and perceptual quality in diffusion and flow-matching generators. A critical step for applying online RL to flow matching is turning the deterministic sampling trajectory into a stochastic policy, typically by replacing the reverse-time Ordinary Differential Equation (ODE) with a Stochastic Differential Equation (SDE). The stochastic sampler, controlling the exploration behavior and denoising dynamics, is thus part of the policy, and its design can significantly a

Source: arXiv cs.LG — read the full report at the original publisher.

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