DFPO: Scaling Value Modeling via Distributional Flow towards Robust and Generalizable LLM Post-Training

• URL: http://arxiv.org/abs/2602.05890v1
• Date: Thu, 05 Feb 2026 17:07:42 GMT
• Title: DFPO: Scaling Value Modeling via Distributional Flow towards Robust and Generalizable LLM Post-Training
• Authors: Dingwei Zhu, Zhiheng Xi, Shihan Dou, Jiahan Li, Chenhao Huang, Junjie Ye, Sixian Li, Mingxu Chai, Yuhui Wang, Yajie Yang, Ming Zhang, Jiazheng Zhang, Shichun Liu, Caishuang Huang, Yunke Zhang, Yuran Wang, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang,
• Abstract summary: Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain generalization.<n>Recent distributional RL methods improve robustness by modeling values with multiple quantile points, but they still learn each quantile independently as a scalar.<n>We propose DFPO, a robust distributional RL framework that models values as continuous flows across time steps.
• Score: 94.568675548967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain (OOD) generalization, especially in LLM post-training. Recent distributional RL methods improve robustness by modeling values with multiple quantile points, but they still learn each quantile independently as a scalar. This results in rough-grained value representations that lack fine-grained conditioning on state information, struggling under complex and OOD conditions. We propose DFPO (Distributional Value Flow Policy Optimization with Conditional Risk and Consistency Control), a robust distributional RL framework that models values as continuous flows across time steps. By scaling value modeling through learning of a value flow field instead of isolated quantile predictions, DFPO captures richer state information for more accurate advantage estimation. To stabilize training under noisy feedback, DFPO further integrates conditional risk control and consistency constraints along value flow trajectories. Experiments on dialogue, math reasoning, and scientific tasks show that DFPO outperforms PPO, FlowRL, and other robust baselines under noisy supervision, achieving improved training stability and generalization.

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