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        <title>Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting</title>
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        <namePart type="given">Yining</namePart>
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    <abstract>Prior works in multi-objective reinforcement learning typically use linear reward scalarization with fixed weights, which provably fail to capture non-convex Pareto fronts and thus yield suboptimal results. This limitation becomes especially critical in online preference alignment for large language models. Here, stochastic trajectories generated by parameterized policies create highly non-linear and non-convex mappings from parameters to objectives that no single static weighting scheme can find optimal trade-offs. We address this limitation by introducing dynamic reward weighting, which adaptively adjusts reward weights during the online reinforcement learning process. Unlike existing approaches that rely on fixed-weight interpolation, our dynamic weighting continuously balances and prioritizes objectives in training, facilitating effective exploration of Pareto fronts in objective space. We introduce two approaches of increasing sophistication and generalizability: hypervolume-guided weight adaptation and gradient-based weight optimization, offering a versatile toolkit for online multi-objective alignment. Our extensive experiments demonstrate their compatibility with commonly used online reinforcement learning algorithms, effectiveness across multiple datasets, and applicability to different model families, consistently achieving Pareto dominant solutions with fewer training steps than fixed-weight linear scalarization baselines.</abstract>
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    <identifier type="doi">10.1162/tacl.a.696</identifier>
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%0 Journal Article
%T Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting
%A Lu, Yining
%A Wang, Zilong
%A Li, Shiyang
%A Liu, Xin
%A Yu, Changlong
%A Yin, Qingyu
%A Shi, Zhan
%A Zhang, Zixuan
%A Jiang, Meng
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F lu-etal-2026-learning
%X Prior works in multi-objective reinforcement learning typically use linear reward scalarization with fixed weights, which provably fail to capture non-convex Pareto fronts and thus yield suboptimal results. This limitation becomes especially critical in online preference alignment for large language models. Here, stochastic trajectories generated by parameterized policies create highly non-linear and non-convex mappings from parameters to objectives that no single static weighting scheme can find optimal trade-offs. We address this limitation by introducing dynamic reward weighting, which adaptively adjusts reward weights during the online reinforcement learning process. Unlike existing approaches that rely on fixed-weight interpolation, our dynamic weighting continuously balances and prioritizes objectives in training, facilitating effective exploration of Pareto fronts in objective space. We introduce two approaches of increasing sophistication and generalizability: hypervolume-guided weight adaptation and gradient-based weight optimization, offering a versatile toolkit for online multi-objective alignment. Our extensive experiments demonstrate their compatibility with commonly used online reinforcement learning algorithms, effectiveness across multiple datasets, and applicability to different model families, consistently achieving Pareto dominant solutions with fewer training steps than fixed-weight linear scalarization baselines.
%R 10.1162/tacl.a.696
%U https://aclanthology.org/2026.tacl-1.47/
%U https://doi.org/10.1162/tacl.a.696
%P 1051-1073

Markdown (Informal)

[Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting](https://aclanthology.org/2026.tacl-1.47/) (Lu et al., TACL 2026)

• Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting (Lu et al., TACL 2026)

ACL

• Yining Lu, Zilong Wang, Shiyang Li, Xin Liu, Changlong Yu, Qingyu Yin, Zhan Shi, Zixuan Zhang, and Meng Jiang. 2026. Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting. Transactions of the Association for Computational Linguistics, 14:1051–1073.