@inproceedings{wu-etal-2026-imbalanced,
title = "Imbalanced Gradients in {RL} Post-Training of Multi-Task {LLM}s",
author = "Wu, Runzhe and
Samanta, Ankur and
Jain, Ayush and
Fujimoto, Scott and
Kwon, Jeongyeol and
Kretzu, Ben and
Yu, Youliang and
Hassani, Kaveh and
Vidolov, Boris and
Efroni, Yonathan",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.164/",
pages = "3137--3150",
ISBN = "979-8-89176-386-9",
abstract = "Multi-task post-training of large language models (LLMs) is typically performed by mixing datasets from different tasks and optimizing them jointly. This approach implicitly assumes that all tasks contribute gradients of similar magnitudes; when this assumption fails, optimization becomes biased toward large-gradient tasks. In this paper, however, we show that this assumption fails in RL post-training: certain tasks produce significantly larger gradients, thus biasing updates toward those tasks. Such gradient imbalance would be justified only if larger gradients implied larger learning gains on the tasks (i.e., larger performance improvements){---}but we find this is not true. Large-gradient tasks can achieve similar or even much lower learning gains than small-gradient ones. Further analyses reveal that these gradient imbalances cannot be explained by typical training statistics such as training rewards or advantages, suggesting that they arise from the *inherent* differences between tasks. This cautions against naive dataset mixing and calls for future work on principled gradient-level corrections for LLMs."
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<abstract>Multi-task post-training of large language models (LLMs) is typically performed by mixing datasets from different tasks and optimizing them jointly. This approach implicitly assumes that all tasks contribute gradients of similar magnitudes; when this assumption fails, optimization becomes biased toward large-gradient tasks. In this paper, however, we show that this assumption fails in RL post-training: certain tasks produce significantly larger gradients, thus biasing updates toward those tasks. Such gradient imbalance would be justified only if larger gradients implied larger learning gains on the tasks (i.e., larger performance improvements)—but we find this is not true. Large-gradient tasks can achieve similar or even much lower learning gains than small-gradient ones. Further analyses reveal that these gradient imbalances cannot be explained by typical training statistics such as training rewards or advantages, suggesting that they arise from the *inherent* differences between tasks. This cautions against naive dataset mixing and calls for future work on principled gradient-level corrections for LLMs.</abstract>
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%0 Conference Proceedings
%T Imbalanced Gradients in RL Post-Training of Multi-Task LLMs
%A Wu, Runzhe
%A Samanta, Ankur
%A Jain, Ayush
%A Fujimoto, Scott
%A Kwon, Jeongyeol
%A Kretzu, Ben
%A Yu, Youliang
%A Hassani, Kaveh
%A Vidolov, Boris
%A Efroni, Yonathan
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F wu-etal-2026-imbalanced
%X Multi-task post-training of large language models (LLMs) is typically performed by mixing datasets from different tasks and optimizing them jointly. This approach implicitly assumes that all tasks contribute gradients of similar magnitudes; when this assumption fails, optimization becomes biased toward large-gradient tasks. In this paper, however, we show that this assumption fails in RL post-training: certain tasks produce significantly larger gradients, thus biasing updates toward those tasks. Such gradient imbalance would be justified only if larger gradients implied larger learning gains on the tasks (i.e., larger performance improvements)—but we find this is not true. Large-gradient tasks can achieve similar or even much lower learning gains than small-gradient ones. Further analyses reveal that these gradient imbalances cannot be explained by typical training statistics such as training rewards or advantages, suggesting that they arise from the *inherent* differences between tasks. This cautions against naive dataset mixing and calls for future work on principled gradient-level corrections for LLMs.
%U https://aclanthology.org/2026.findings-eacl.164/
%P 3137-3150
Markdown (Informal)
[Imbalanced Gradients in RL Post-Training of Multi-Task LLMs](https://aclanthology.org/2026.findings-eacl.164/) (Wu et al., Findings 2026)
ACL
- Runzhe Wu, Ankur Samanta, Ayush Jain, Scott Fujimoto, Jeongyeol Kwon, Ben Kretzu, Youliang Yu, Kaveh Hassani, Boris Vidolov, and Yonathan Efroni. 2026. Imbalanced Gradients in RL Post-Training of Multi-Task LLMs. In Findings of the Association for Computational Linguistics: EACL 2026, pages 3137–3150, Rabat, Morocco. Association for Computational Linguistics.