@inproceedings{zhu-etal-2026-vrpo,
title = "{VRPO}: Rethinking Value Modeling for Robust {RL} under Noisy Supervision in {LLM} Post-Training",
author = "Zhu, Dingwei and
Dou, Shihan and
Xi, Zhiheng and
Jin, Senjie and
Zhang, Guoqiang and
Zhang, Jiazheng and
Ye, Junjie and
Chai, Mingxu and
Zhou, Enyu and
Zhang, Ming and
Wang, Yuhui and
Huang, Caishuang and
Huang, Chenhao and
Zhang, Yunke and
Wang, Yuran and
Gui, Tao and
Zhang, Qi and
Qiu, Xipeng and
Huang, Xuanjing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1103/",
pages = "24046--24067",
ISBN = "979-8-89176-390-6",
abstract = "Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete reward supervision, which undermines policy stability and generalization. Such noise may cause models to ignore key information or even collapse in advantage estimation. We find that a strong value model is essential for absorbing unstable signals and producing reliable advantages, offering denser and more robust supervision than the reward model. To better optimize noisy supervision, we propose VRPO, a framework that enhances value modeling for robust RL in LLM post-training. VRPO integrates (1) auxiliary losses guided by entropy and perplexity from a frozen language model, and (2) a variational information bottleneck, enabling the value model to filter noise and capture key words. This design allows the value model to correct noise rewards and generate more reliable advantage estimates, transforming it from a passive predictor into an active noise regulator. Experiments on multi-turn dialogue, math reasoning, and science QA with both rule-based and model-based rewards show that VRPO consistently outperforms baselines such as PPO and GRPO. Our work highlight the central role of the value model in Robust RL and provide a principled and practical approach to policy optimization under noisy supervision."
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<abstract>Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete reward supervision, which undermines policy stability and generalization. Such noise may cause models to ignore key information or even collapse in advantage estimation. We find that a strong value model is essential for absorbing unstable signals and producing reliable advantages, offering denser and more robust supervision than the reward model. To better optimize noisy supervision, we propose VRPO, a framework that enhances value modeling for robust RL in LLM post-training. VRPO integrates (1) auxiliary losses guided by entropy and perplexity from a frozen language model, and (2) a variational information bottleneck, enabling the value model to filter noise and capture key words. This design allows the value model to correct noise rewards and generate more reliable advantage estimates, transforming it from a passive predictor into an active noise regulator. Experiments on multi-turn dialogue, math reasoning, and science QA with both rule-based and model-based rewards show that VRPO consistently outperforms baselines such as PPO and GRPO. Our work highlight the central role of the value model in Robust RL and provide a principled and practical approach to policy optimization under noisy supervision.</abstract>
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%0 Conference Proceedings
%T VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training
%A Zhu, Dingwei
%A Dou, Shihan
%A Xi, Zhiheng
%A Jin, Senjie
%A Zhang, Guoqiang
%A Zhang, Jiazheng
%A Ye, Junjie
%A Chai, Mingxu
%A Zhou, Enyu
%A Zhang, Ming
%A Wang, Yuhui
%A Huang, Caishuang
%A Huang, Chenhao
%A Zhang, Yunke
%A Wang, Yuran
%A Gui, Tao
%A Zhang, Qi
%A Qiu, Xipeng
%A Huang, Xuanjing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhu-etal-2026-vrpo
%X Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete reward supervision, which undermines policy stability and generalization. Such noise may cause models to ignore key information or even collapse in advantage estimation. We find that a strong value model is essential for absorbing unstable signals and producing reliable advantages, offering denser and more robust supervision than the reward model. To better optimize noisy supervision, we propose VRPO, a framework that enhances value modeling for robust RL in LLM post-training. VRPO integrates (1) auxiliary losses guided by entropy and perplexity from a frozen language model, and (2) a variational information bottleneck, enabling the value model to filter noise and capture key words. This design allows the value model to correct noise rewards and generate more reliable advantage estimates, transforming it from a passive predictor into an active noise regulator. Experiments on multi-turn dialogue, math reasoning, and science QA with both rule-based and model-based rewards show that VRPO consistently outperforms baselines such as PPO and GRPO. Our work highlight the central role of the value model in Robust RL and provide a principled and practical approach to policy optimization under noisy supervision.
%U https://aclanthology.org/2026.acl-long.1103/
%P 24046-24067
Markdown (Informal)
[VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training](https://aclanthology.org/2026.acl-long.1103/) (Zhu et al., ACL 2026)
ACL
- Dingwei Zhu, Shihan Dou, Zhiheng Xi, Senjie Jin, Guoqiang Zhang, Jiazheng Zhang, Junjie Ye, Mingxu Chai, Enyu Zhou, Ming Zhang, Yuhui Wang, Caishuang Huang, Chenhao Huang, Yunke Zhang, Yuran Wang, Tao Gui, Qi Zhang, Xipeng Qiu, and Xuanjing Huang. 2026. VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24046–24067, San Diego, California, United States. Association for Computational Linguistics.