@inproceedings{zeng-etal-2026-balanced,
title = "Balanced Aggregation: Understanding and Fixing Aggregation Bias in {GRPO}",
author = "Zeng, Zhiyuan and
Huang, Jiameng and
Yin, Zhangyue and
Li, Bingrui and
Liu, Jiashuo and
Wu, Yuhao and
Zheng, Yining and
Zhang, Ge and
Huang, Wenhao and
Qiu, Xipeng and
Li, Ziniu",
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.2120/",
pages = "45195--45207",
ISBN = "979-8-89176-462-0",
abstract = "Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language models, and GRPO-style training is widely adopted for its simplicity and effectiveness. However, an important design choice remains underexplored: how token-level policy gradient terms are aggregated within each sampled group. Standard GRPO uses sequence aggregation, while recent work has advocated token aggregation as a better alternative. We show that these two rules induce different optimization biases: token aggregation introduces sign-length coupling, while sequence aggregation implicitly downweights longer responses through sequence-level equal weighting. To address this tension, we propose \textbf{Balanced Aggregation (BA)}, a simple drop-in replacement that computes token-level means separately within the positive and negative subsets and then combines them with sequence-count-based weights. Experiments with Qwen2.5-Math-7B and Qwen3-1.7B on DAPO-17k and Polaris, evaluated on six reasoning and coding benchmarks, show that BA consistently improves training stability and final performance over standard token and sequence aggregation. Our analysis further shows that the relative effectiveness of token and sequence aggregation is largely governed by response-length variation and the positive-negative length gap, highlighting aggregation as a critical design dimension in GRPO-style RLVR."
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<abstract>Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language models, and GRPO-style training is widely adopted for its simplicity and effectiveness. However, an important design choice remains underexplored: how token-level policy gradient terms are aggregated within each sampled group. Standard GRPO uses sequence aggregation, while recent work has advocated token aggregation as a better alternative. We show that these two rules induce different optimization biases: token aggregation introduces sign-length coupling, while sequence aggregation implicitly downweights longer responses through sequence-level equal weighting. To address this tension, we propose Balanced Aggregation (BA), a simple drop-in replacement that computes token-level means separately within the positive and negative subsets and then combines them with sequence-count-based weights. Experiments with Qwen2.5-Math-7B and Qwen3-1.7B on DAPO-17k and Polaris, evaluated on six reasoning and coding benchmarks, show that BA consistently improves training stability and final performance over standard token and sequence aggregation. Our analysis further shows that the relative effectiveness of token and sequence aggregation is largely governed by response-length variation and the positive-negative length gap, highlighting aggregation as a critical design dimension in GRPO-style RLVR.</abstract>
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%0 Conference Proceedings
%T Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO
%A Zeng, Zhiyuan
%A Huang, Jiameng
%A Yin, Zhangyue
%A Li, Bingrui
%A Liu, Jiashuo
%A Wu, Yuhao
%A Zheng, Yining
%A Zhang, Ge
%A Huang, Wenhao
%A Qiu, Xipeng
%A Li, Ziniu
%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 zeng-etal-2026-balanced
%X Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language models, and GRPO-style training is widely adopted for its simplicity and effectiveness. However, an important design choice remains underexplored: how token-level policy gradient terms are aggregated within each sampled group. Standard GRPO uses sequence aggregation, while recent work has advocated token aggregation as a better alternative. We show that these two rules induce different optimization biases: token aggregation introduces sign-length coupling, while sequence aggregation implicitly downweights longer responses through sequence-level equal weighting. To address this tension, we propose Balanced Aggregation (BA), a simple drop-in replacement that computes token-level means separately within the positive and negative subsets and then combines them with sequence-count-based weights. Experiments with Qwen2.5-Math-7B and Qwen3-1.7B on DAPO-17k and Polaris, evaluated on six reasoning and coding benchmarks, show that BA consistently improves training stability and final performance over standard token and sequence aggregation. Our analysis further shows that the relative effectiveness of token and sequence aggregation is largely governed by response-length variation and the positive-negative length gap, highlighting aggregation as a critical design dimension in GRPO-style RLVR.
%U https://aclanthology.org/2026.findings-emnlp.2120/
%P 45195-45207
Markdown (Informal)
[Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO](https://aclanthology.org/2026.findings-emnlp.2120/) (Zeng et al., Findings 2026)
ACL
- Zhiyuan Zeng, Jiameng Huang, Zhangyue Yin, Bingrui Li, Jiashuo Liu, Yuhao Wu, Yining Zheng, Ge Zhang, Wenhao Huang, Xipeng Qiu, and Ziniu Li. 2026. Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 45195–45207, Budapest, Hungary. Association for Computational Linguistics.