@inproceedings{wu-etal-2026-preferences,
title = "Not All Preferences Deserve Gradients: Understanding Gradient Utility in Offline Reasoning Alignment",
author = "Wu, Hui and
Cai, Hengyi and
Zhao, Jinman and
Chen, Xinran and
Li, Ziheng and
Zhao, Zhejun and
Wang, Shuaiqiang and
Li, Yuchen and
Yin, Dawei",
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.2077/",
pages = "47316--47330",
ISBN = "979-8-89176-459-0",
abstract = "Offline preference optimization aligns reasoning models from fixed chosen-rejected pairs, yet standard methods apply gradient updates from every pair regardless of its training value under the current policy. We argue that this uniform treatment is wasteful and potentially harmful. From the perspective of gradient utility, we show that a pair{'}s contribution depends jointly on informativeness and stability. Pair utility drifts as the policy evolves, high-gradient samples can coincide with high-curvature regions, leading to noisy and destabilizing updates, and the most effective supervision comes from stable confident errors where the model is reliably wrong yet curvature remains low. These findings motivate **SAGE** (Stability-Aware Gradient Efficiency), which maintains difficulty-stratified candidate pools refreshed during training and selects pairs within each pool by a forward-pass signal-to-curvature score. Only pairs with high current utility receive gradient computation; the rest are excluded from backpropagation. On mathematical reasoning benchmarks across multiple model scales, SAGE outperforms full-data and size-matched baselines while producing substantially smoother optimization trajectories."
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<abstract>Offline preference optimization aligns reasoning models from fixed chosen-rejected pairs, yet standard methods apply gradient updates from every pair regardless of its training value under the current policy. We argue that this uniform treatment is wasteful and potentially harmful. From the perspective of gradient utility, we show that a pair’s contribution depends jointly on informativeness and stability. Pair utility drifts as the policy evolves, high-gradient samples can coincide with high-curvature regions, leading to noisy and destabilizing updates, and the most effective supervision comes from stable confident errors where the model is reliably wrong yet curvature remains low. These findings motivate **SAGE** (Stability-Aware Gradient Efficiency), which maintains difficulty-stratified candidate pools refreshed during training and selects pairs within each pool by a forward-pass signal-to-curvature score. Only pairs with high current utility receive gradient computation; the rest are excluded from backpropagation. On mathematical reasoning benchmarks across multiple model scales, SAGE outperforms full-data and size-matched baselines while producing substantially smoother optimization trajectories.</abstract>
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%0 Conference Proceedings
%T Not All Preferences Deserve Gradients: Understanding Gradient Utility in Offline Reasoning Alignment
%A Wu, Hui
%A Cai, Hengyi
%A Zhao, Jinman
%A Chen, Xinran
%A Li, Ziheng
%A Zhao, Zhejun
%A Wang, Shuaiqiang
%A Li, Yuchen
%A Yin, Dawei
%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 wu-etal-2026-preferences
%X Offline preference optimization aligns reasoning models from fixed chosen-rejected pairs, yet standard methods apply gradient updates from every pair regardless of its training value under the current policy. We argue that this uniform treatment is wasteful and potentially harmful. From the perspective of gradient utility, we show that a pair’s contribution depends jointly on informativeness and stability. Pair utility drifts as the policy evolves, high-gradient samples can coincide with high-curvature regions, leading to noisy and destabilizing updates, and the most effective supervision comes from stable confident errors where the model is reliably wrong yet curvature remains low. These findings motivate **SAGE** (Stability-Aware Gradient Efficiency), which maintains difficulty-stratified candidate pools refreshed during training and selects pairs within each pool by a forward-pass signal-to-curvature score. Only pairs with high current utility receive gradient computation; the rest are excluded from backpropagation. On mathematical reasoning benchmarks across multiple model scales, SAGE outperforms full-data and size-matched baselines while producing substantially smoother optimization trajectories.
%U https://aclanthology.org/2026.emnlp-main.2077/
%P 47316-47330
Markdown (Informal)
[Not All Preferences Deserve Gradients: Understanding Gradient Utility in Offline Reasoning Alignment](https://aclanthology.org/2026.emnlp-main.2077/) (Wu et al., EMNLP 2026)
ACL
- Hui Wu, Hengyi Cai, Jinman Zhao, Xinran Chen, Ziheng Li, Zhejun Zhao, Shuaiqiang Wang, Yuchen Li, and Dawei Yin. 2026. Not All Preferences Deserve Gradients: Understanding Gradient Utility in Offline Reasoning Alignment. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 47316–47330, Budapest, Hungary. Association for Computational Linguistics.