@inproceedings{liu-etal-2026-gd2po,
title = "{GD}$^2${PO}: Group-Dynamic Reward-Decoupled Policy Optimization for Mitigating Multi-Reward {RL} Conflicts",
author = "Liu, Haotian and
Liu, Yihao and
Ni, Jingwei and
Huang, Siyuan and
Liu, Xinpeng and
Cheng, Pengyu and
Song, Jiajun and
Ding, Ruijin and
Li, Junfeng and
Yu, Zhechao and
Zhou, Mengyu and
Xu, Hongteng and
Jiang, Xiaoxi and
Jiang, Guanjun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1070/",
pages = "22624--22644",
ISBN = "979-8-89176-462-0",
abstract = "Post-training large language models often require optimizing multiple objectives simultaneously, motivating multi-reward reinforcement learning (RL) methods that model different objectives as separate reward signals. Existing methods like Group reward-Decoupled Policy Optimization (GDPO) compute group-normalized advantages separately for each reward dimension and then aggregate them for policy updates. However, this aggregation process can cause \textit{cross-reward advantage conflict}: the same rollout may have positive advantages on some reward dimensions but negative advantages on others, causing positive and negative signals to cancel each other during aggregation. To address this issue, we propose GD$^2$PO, short for \textit{Group-Dynamic Reward-Decoupled Policy Optimization}, a conflict-aware multi-reward policy optimization method. Before advantage aggregation, GD$^2$PO uses conflict-aware criteria to identify and filter rollouts with severe reward-wise disagreement, reducing the influence of unreliable update signals. It further introduces query-level reweighting to dynamically adjust the update strength of each query according to its overall reward consensus. Experiments on tool calling and helpfulness-safety alignment show that GD$^2$PO outperforms existing methods. Our code is available at{~}\url{https://github.com/Qwen-Applications/GD2PO}."
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<abstract>Post-training large language models often require optimizing multiple objectives simultaneously, motivating multi-reward reinforcement learning (RL) methods that model different objectives as separate reward signals. Existing methods like Group reward-Decoupled Policy Optimization (GDPO) compute group-normalized advantages separately for each reward dimension and then aggregate them for policy updates. However, this aggregation process can cause cross-reward advantage conflict: the same rollout may have positive advantages on some reward dimensions but negative advantages on others, causing positive and negative signals to cancel each other during aggregation. To address this issue, we propose GD²PO, short for Group-Dynamic Reward-Decoupled Policy Optimization, a conflict-aware multi-reward policy optimization method. Before advantage aggregation, GD²PO uses conflict-aware criteria to identify and filter rollouts with severe reward-wise disagreement, reducing the influence of unreliable update signals. It further introduces query-level reweighting to dynamically adjust the update strength of each query according to its overall reward consensus. Experiments on tool calling and helpfulness-safety alignment show that GD²PO outperforms existing methods. Our code is available at https://github.com/Qwen-Applications/GD2PO.</abstract>
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%0 Conference Proceedings
%T GD²PO: Group-Dynamic Reward-Decoupled Policy Optimization for Mitigating Multi-Reward RL Conflicts
%A Liu, Haotian
%A Liu, Yihao
%A Ni, Jingwei
%A Huang, Siyuan
%A Liu, Xinpeng
%A Cheng, Pengyu
%A Song, Jiajun
%A Ding, Ruijin
%A Li, Junfeng
%A Yu, Zhechao
%A Zhou, Mengyu
%A Xu, Hongteng
%A Jiang, Xiaoxi
%A Jiang, Guanjun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-gd2po
%X Post-training large language models often require optimizing multiple objectives simultaneously, motivating multi-reward reinforcement learning (RL) methods that model different objectives as separate reward signals. Existing methods like Group reward-Decoupled Policy Optimization (GDPO) compute group-normalized advantages separately for each reward dimension and then aggregate them for policy updates. However, this aggregation process can cause cross-reward advantage conflict: the same rollout may have positive advantages on some reward dimensions but negative advantages on others, causing positive and negative signals to cancel each other during aggregation. To address this issue, we propose GD²PO, short for Group-Dynamic Reward-Decoupled Policy Optimization, a conflict-aware multi-reward policy optimization method. Before advantage aggregation, GD²PO uses conflict-aware criteria to identify and filter rollouts with severe reward-wise disagreement, reducing the influence of unreliable update signals. It further introduces query-level reweighting to dynamically adjust the update strength of each query according to its overall reward consensus. Experiments on tool calling and helpfulness-safety alignment show that GD²PO outperforms existing methods. Our code is available at https://github.com/Qwen-Applications/GD2PO.
%U https://aclanthology.org/2026.findings-emnlp.1070/
%P 22624-22644
Markdown (Informal)
[GD2PO: Group-Dynamic Reward-Decoupled Policy Optimization for Mitigating Multi-Reward RL Conflicts](https://aclanthology.org/2026.findings-emnlp.1070/) (Liu et al., Findings 2026)
ACL
- Haotian Liu, Yihao Liu, Jingwei Ni, Siyuan Huang, Xinpeng Liu, Pengyu Cheng, Jiajun Song, Ruijin Ding, Junfeng Li, Zhechao Yu, Mengyu Zhou, Hongteng Xu, Xiaoxi Jiang, and Guanjun Jiang. 2026. GD2PO: Group-Dynamic Reward-Decoupled Policy Optimization for Mitigating Multi-Reward RL Conflicts. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22624–22644, Budapest, Hungary. Association for Computational Linguistics.