@inproceedings{xueyun-etal-2026-learning,
title = "Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization",
author = "Xueyun, Tian and
Ma, MingHua and
Xu, Bingbing and
Lyu, Nuoyan and
Li, Wei and
Dong, Heng and
Chu, Zheng and
Wang, Yuanzhuo and
Shen, Huawei",
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.1370/",
pages = "29689--29711",
ISBN = "979-8-89176-390-6",
abstract = "Supervised fine-tuning (SFT) on chain-of-thought (CoT) trajectories demonstrations is a common approach for enabling reasoning in large language models. Standard practices typically only retain trajectories with correct final answers (*positives*) while ignoring the rest (*negatives*). We argue that this paradigm discards substantial supervision and exacerbates overfitting, limiting out-of-domain (OOD) generalization. Specifically, we surprisingly find that incorporating *negative* trajectories into SFT yields substantial OOD generalization gains over *positive-only* training, as these trajectories often retain valid intermediate reasoning despite incorrect final answers. To understand this effect in depth, we systematically analyze data, training dynamics, and inference behavior, identifying 22 recurring patterns in negative chains that serve a dual role: they moderate loss descent to mitigate overfitting during training and boost policy entropy by 35.67{\%} during inference to facilitate exploration. Motivated by these observations, we further propose **Gain-based LOss Weighting** (GLOW), an adaptive, sample-aware scheme that exploits such distinctive training dynamics by rescaling per-sample loss based on inter-epoch progress. Empirically, GLOW efficiently leverages unfiltered trajectories, yielding a 5.51{\%} OOD gain over positive-only SFT on Qwen2.5-7B and boosting MMLU from 72.82{\%} to 76.47{\%} as an RL initialization. Code is available at [Github](https://github.com/Eureka-Maggie/GLOW)."
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<abstract>Supervised fine-tuning (SFT) on chain-of-thought (CoT) trajectories demonstrations is a common approach for enabling reasoning in large language models. Standard practices typically only retain trajectories with correct final answers (*positives*) while ignoring the rest (*negatives*). We argue that this paradigm discards substantial supervision and exacerbates overfitting, limiting out-of-domain (OOD) generalization. Specifically, we surprisingly find that incorporating *negative* trajectories into SFT yields substantial OOD generalization gains over *positive-only* training, as these trajectories often retain valid intermediate reasoning despite incorrect final answers. To understand this effect in depth, we systematically analyze data, training dynamics, and inference behavior, identifying 22 recurring patterns in negative chains that serve a dual role: they moderate loss descent to mitigate overfitting during training and boost policy entropy by 35.67% during inference to facilitate exploration. Motivated by these observations, we further propose **Gain-based LOss Weighting** (GLOW), an adaptive, sample-aware scheme that exploits such distinctive training dynamics by rescaling per-sample loss based on inter-epoch progress. Empirically, GLOW efficiently leverages unfiltered trajectories, yielding a 5.51% OOD gain over positive-only SFT on Qwen2.5-7B and boosting MMLU from 72.82% to 76.47% as an RL initialization. Code is available at [Github](https://github.com/Eureka-Maggie/GLOW).</abstract>
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%0 Conference Proceedings
%T Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization
%A Xueyun, Tian
%A Ma, MingHua
%A Xu, Bingbing
%A Lyu, Nuoyan
%A Li, Wei
%A Dong, Heng
%A Chu, Zheng
%A Wang, Yuanzhuo
%A Shen, Huawei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%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 xueyun-etal-2026-learning
%X Supervised fine-tuning (SFT) on chain-of-thought (CoT) trajectories demonstrations is a common approach for enabling reasoning in large language models. Standard practices typically only retain trajectories with correct final answers (*positives*) while ignoring the rest (*negatives*). We argue that this paradigm discards substantial supervision and exacerbates overfitting, limiting out-of-domain (OOD) generalization. Specifically, we surprisingly find that incorporating *negative* trajectories into SFT yields substantial OOD generalization gains over *positive-only* training, as these trajectories often retain valid intermediate reasoning despite incorrect final answers. To understand this effect in depth, we systematically analyze data, training dynamics, and inference behavior, identifying 22 recurring patterns in negative chains that serve a dual role: they moderate loss descent to mitigate overfitting during training and boost policy entropy by 35.67% during inference to facilitate exploration. Motivated by these observations, we further propose **Gain-based LOss Weighting** (GLOW), an adaptive, sample-aware scheme that exploits such distinctive training dynamics by rescaling per-sample loss based on inter-epoch progress. Empirically, GLOW efficiently leverages unfiltered trajectories, yielding a 5.51% OOD gain over positive-only SFT on Qwen2.5-7B and boosting MMLU from 72.82% to 76.47% as an RL initialization. Code is available at [Github](https://github.com/Eureka-Maggie/GLOW).
%U https://aclanthology.org/2026.acl-long.1370/
%P 29689-29711
Markdown (Informal)
[Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization](https://aclanthology.org/2026.acl-long.1370/) (Xueyun et al., ACL 2026)
ACL
- Tian Xueyun, MingHua Ma, Bingbing Xu, Nuoyan Lyu, Wei Li, Heng Dong, Zheng Chu, Yuanzhuo Wang, and Huawei Shen. 2026. Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29689–29711, San Diego, California, United States. Association for Computational Linguistics.