@inproceedings{lin-etal-2026-dwdpo,
title = "dw{DPO}: Training Multi-Turn {LLM} Agents via Divergence-Weighted Direct Preference Optimization",
author = "Lin, Jinghao and
Wu, Yuhang and
Wang, Yuanqing and
Li, Yuchen and
Kang, Tianxingjian and
Coskunuzer, Baris and
Zheng, Xiawu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2406/",
pages = "54657--54674",
ISBN = "979-8-89176-459-0",
abstract = "Large language models deployed as multi-step agents are increasingly trained via Direct Preference Optimization (DPO) on trajectory-level comparisons. Yet standard DPO weights every step uniformly, penalizing correct early actions in a losing trajectory as severely as the causal error itself. We propose Divergence-Weighted DPO(dwDPO), a drop-in replacement that detects the first divergence step between competing trajectories using implicit rewards already computed by DPO, then concentrates the loss around it via an asymmetric kernel with a closed-form scale factor that preserves the Bradley-Terry preference model. dwDPO recovers standard DPO as a special case and provably achieves higher gradient concentration at the critical step than both uniform and monotonic-discount baselines. Extensive experimental results across six deep-research benchmarks demonstrate the superiority of dwDPO over state-of-the-art open-source baselines of comparable scale at no additional inference cost."
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<abstract>Large language models deployed as multi-step agents are increasingly trained via Direct Preference Optimization (DPO) on trajectory-level comparisons. Yet standard DPO weights every step uniformly, penalizing correct early actions in a losing trajectory as severely as the causal error itself. We propose Divergence-Weighted DPO(dwDPO), a drop-in replacement that detects the first divergence step between competing trajectories using implicit rewards already computed by DPO, then concentrates the loss around it via an asymmetric kernel with a closed-form scale factor that preserves the Bradley-Terry preference model. dwDPO recovers standard DPO as a special case and provably achieves higher gradient concentration at the critical step than both uniform and monotonic-discount baselines. Extensive experimental results across six deep-research benchmarks demonstrate the superiority of dwDPO over state-of-the-art open-source baselines of comparable scale at no additional inference cost.</abstract>
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%0 Conference Proceedings
%T dwDPO: Training Multi-Turn LLM Agents via Divergence-Weighted Direct Preference Optimization
%A Lin, Jinghao
%A Wu, Yuhang
%A Wang, Yuanqing
%A Li, Yuchen
%A Kang, Tianxingjian
%A Coskunuzer, Baris
%A Zheng, Xiawu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lin-etal-2026-dwdpo
%X Large language models deployed as multi-step agents are increasingly trained via Direct Preference Optimization (DPO) on trajectory-level comparisons. Yet standard DPO weights every step uniformly, penalizing correct early actions in a losing trajectory as severely as the causal error itself. We propose Divergence-Weighted DPO(dwDPO), a drop-in replacement that detects the first divergence step between competing trajectories using implicit rewards already computed by DPO, then concentrates the loss around it via an asymmetric kernel with a closed-form scale factor that preserves the Bradley-Terry preference model. dwDPO recovers standard DPO as a special case and provably achieves higher gradient concentration at the critical step than both uniform and monotonic-discount baselines. Extensive experimental results across six deep-research benchmarks demonstrate the superiority of dwDPO over state-of-the-art open-source baselines of comparable scale at no additional inference cost.
%U https://aclanthology.org/2026.emnlp-main.2406/
%P 54657-54674
Markdown (Informal)
[dwDPO: Training Multi-Turn LLM Agents via Divergence-Weighted Direct Preference Optimization](https://aclanthology.org/2026.emnlp-main.2406/) (Lin et al., EMNLP 2026)
ACL