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arXiv · 2610.15780

FlowGRPO for Vocal Separation: From Reward Analysis to Reward Design

Lishi Zuo·Chong-Xin Gan·Zezhong Jin·Youzhi Tu·Man-Wai Mak·KongAik Lee
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Abstract

The Flow Matching objective supervises the velocity field at intermediate states, whereas waveform metrics are evaluated after decoding and may be non-differentiable. To align Flow Matching models with waveform-level and perceptual quality objectives during post-training, FlowGRPO optimizes their trajectories using endpoint rewards. In this work, we focus on music separation and use multi-resolution short-time Fourier transform (MRSTFT), scale-invariant signal-to-distortion ratio (SI-SDR), and Virtual Speech Quality Objective Listener (ViSQOL) as reward components. Before policy optimization, we analyze the relationship between data quality factors and the within-group gap between the maximum and mean rollout rewards for each component. This analysis motivates a revised ViSQOL reward that matches channel-wise root-mean-square (RMS) levels and evaluates the interval from the first to the last active frame, retaining inactive frames within that interval. We also apply a sample-quality filter based on predefined waveform checks. Together, the revised reward and quality filter enable FlowGRPO to improve the pretrained vocal separator in both separation performance and perceptual quality.

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Lishi Zuo, Chong-Xin Gan, Zezhong Jin, Youzhi Tu, Man-Wai Mak, KongAik Lee. 2026-10-11. FlowGRPO for Vocal Separation: From Reward Analysis to Reward Design. https://arxiv.org/abs/2610.15780

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