Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO

• URL: http://arxiv.org/abs/2602.06422v1
• Date: Fri, 06 Feb 2026 06:37:10 GMT
• Title: Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO
• Authors: Yunze Tong, Mushui Liu, Canyu Zhao, Wanggui He, Shiyi Zhang, Hongwei Zhang, Peng Zhang, Jinlong Liu, Ju Huang, Jiamang Wang, Hao Jiang, Pipei Huang,
• Abstract summary: TP-GRPO replaces outcome-based rewards with step-level incremental rewards.<n>It identifies turning points-steps that flip the local reward trend.<n>Turning points are detected solely via sign changes in incremental rewards.
• Score: 20.13873375670213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying GRPO on Flow Matching models has proven effective for text-to-image generation. However, existing paradigms typically propagate an outcome-based reward to all preceding denoising steps without distinguishing the local effect of each step. Moreover, current group-wise ranking mainly compares trajectories at matched timesteps and ignores within-trajectory dependencies, where certain early denoising actions can affect later states via delayed, implicit interactions. We propose TurningPoint-GRPO (TP-GRPO), a GRPO framework that alleviates step-wise reward sparsity and explicitly models long-term effects within the denoising trajectory. TP-GRPO makes two key innovations: (i) it replaces outcome-based rewards with step-level incremental rewards, providing a dense, step-aware learning signal that better isolates each denoising action's "pure" effect, and (ii) it identifies turning points-steps that flip the local reward trend and make subsequent reward evolution consistent with the overall trajectory trend-and assigns these actions an aggregated long-term reward to capture their delayed impact. Turning points are detected solely via sign changes in incremental rewards, making TP-GRPO efficient and hyperparameter-free. Extensive experiments also demonstrate that TP-GRPO exploits reward signals more effectively and consistently improves generation. Demo code is available at https://github.com/YunzeTong/TurningPoint-GRPO.

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