@inproceedings{zhi-etal-2026-spard,
title = "{SPARD}: Self-Paced Curriculum for {RL} Alignment via Integrating Reward Dynamics and Data Utility",
author = "Zhi, Xuyang and
Zhou, Peilun and
Lu, Chengqiang and
Lv, Hang and
Liang, Yiwei and
Zhang, Rongyang and
Gao, Yan and
Yiwu and
Hu, Yao and
Gu, Hongchao and
Lian, Defu and
Wang, Hao and
Chen, Enhong",
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.2191/",
pages = "47402--47422",
ISBN = "979-8-89176-390-6",
abstract = "The evolution of Large Language Models (LLMs) is shifting the focus from single, verifiable tasks toward complex, open-ended real-world scenarios, imposing significant challenges on the post-training phase. In these settings, the scale and complexity of reward systems have grown significantly, transitioning toward multi-objective formulations that encompass a comprehensive spectrum of model capabilities and application contexts. However, traditional methods typically rely on fixed reward weights, ignoring non-stationary learning dynamics and struggling with data heterogeneity across dimensions. To address these issues, we propose SPARD, a framework that establishes an automated, self-paced curriculum by perceiving learning progress to dynamically adjust multi-objective reward weights and data importance, thereby synchronizing learning intent with data utility for optimal performance. Extensive experiments across multiple benchmarks demonstrate that SPARD significantly enhances model capabilities across all domains. Our code is publicly available at https://github.com/USTC-StarTeam/SPARD."
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<abstract>The evolution of Large Language Models (LLMs) is shifting the focus from single, verifiable tasks toward complex, open-ended real-world scenarios, imposing significant challenges on the post-training phase. In these settings, the scale and complexity of reward systems have grown significantly, transitioning toward multi-objective formulations that encompass a comprehensive spectrum of model capabilities and application contexts. However, traditional methods typically rely on fixed reward weights, ignoring non-stationary learning dynamics and struggling with data heterogeneity across dimensions. To address these issues, we propose SPARD, a framework that establishes an automated, self-paced curriculum by perceiving learning progress to dynamically adjust multi-objective reward weights and data importance, thereby synchronizing learning intent with data utility for optimal performance. Extensive experiments across multiple benchmarks demonstrate that SPARD significantly enhances model capabilities across all domains. Our code is publicly available at https://github.com/USTC-StarTeam/SPARD.</abstract>
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%0 Conference Proceedings
%T SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility
%A Zhi, Xuyang
%A Zhou, Peilun
%A Lu, Chengqiang
%A Lv, Hang
%A Liang, Yiwei
%A Zhang, Rongyang
%A Gao, Yan
%A Hu, Yao
%A Gu, Hongchao
%A Lian, Defu
%A Wang, Hao
%A Chen, Enhong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Yiwu
%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 zhi-etal-2026-spard
%X The evolution of Large Language Models (LLMs) is shifting the focus from single, verifiable tasks toward complex, open-ended real-world scenarios, imposing significant challenges on the post-training phase. In these settings, the scale and complexity of reward systems have grown significantly, transitioning toward multi-objective formulations that encompass a comprehensive spectrum of model capabilities and application contexts. However, traditional methods typically rely on fixed reward weights, ignoring non-stationary learning dynamics and struggling with data heterogeneity across dimensions. To address these issues, we propose SPARD, a framework that establishes an automated, self-paced curriculum by perceiving learning progress to dynamically adjust multi-objective reward weights and data importance, thereby synchronizing learning intent with data utility for optimal performance. Extensive experiments across multiple benchmarks demonstrate that SPARD significantly enhances model capabilities across all domains. Our code is publicly available at https://github.com/USTC-StarTeam/SPARD.
%U https://aclanthology.org/2026.acl-long.2191/
%P 47402-47422
Markdown (Informal)
[SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility](https://aclanthology.org/2026.acl-long.2191/) (Zhi et al., ACL 2026)
ACL
- Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, and Enhong Chen. 2026. SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 47402–47422, San Diego, California, United States. Association for Computational Linguistics.