@inproceedings{chen-etal-2026-beyond-polarization,
title = "Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking",
author = "Chen, Xiaoyang and
Liu, Jie and
Liang, Haijin and
Shi, Haibo and
Ma, Jin and
He, Ben and
Sun, Yingfei and
Ye, Dezhi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.987/",
doi = "10.18653/2026.findings-emnlp.987",
pages = "20854--20865",
ISBN = "979-8-89176-462-0",
abstract = "In pointwise document reranking, Chain-of-Thought models typically underperform direct scoring models. While existing diagnostics attribute this to inferior classification, score polarization, or calibration breakdown, whether targeted training can bridge this gap remains unclear. Our empirical study first confirms that this gap is stable across scales up to 32B parameters, ruling out model and data capacity confounders. We then apply stress tests utilizing reinforcement learning, fine-grained supervision, and architectural decoupling to explicitly repair these deviations. Although these interventions improve classification accuracy and absolute scores, the relative ranking gap persists. These findings suggest that, within the pointwise scoring paradigm, routing continuous relevance semantics through discrete text constrains ranking signal resolution, revealing a bottleneck that is stable and difficult to overcome under current standard methods, rather than an easily resolvable training bias."
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<abstract>In pointwise document reranking, Chain-of-Thought models typically underperform direct scoring models. While existing diagnostics attribute this to inferior classification, score polarization, or calibration breakdown, whether targeted training can bridge this gap remains unclear. Our empirical study first confirms that this gap is stable across scales up to 32B parameters, ruling out model and data capacity confounders. We then apply stress tests utilizing reinforcement learning, fine-grained supervision, and architectural decoupling to explicitly repair these deviations. Although these interventions improve classification accuracy and absolute scores, the relative ranking gap persists. These findings suggest that, within the pointwise scoring paradigm, routing continuous relevance semantics through discrete text constrains ranking signal resolution, revealing a bottleneck that is stable and difficult to overcome under current standard methods, rather than an easily resolvable training bias.</abstract>
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%0 Conference Proceedings
%T Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking
%A Chen, Xiaoyang
%A Liu, Jie
%A Liang, Haijin
%A Shi, Haibo
%A Ma, Jin
%A He, Ben
%A Sun, Yingfei
%A Ye, Dezhi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-beyond-polarization
%X In pointwise document reranking, Chain-of-Thought models typically underperform direct scoring models. While existing diagnostics attribute this to inferior classification, score polarization, or calibration breakdown, whether targeted training can bridge this gap remains unclear. Our empirical study first confirms that this gap is stable across scales up to 32B parameters, ruling out model and data capacity confounders. We then apply stress tests utilizing reinforcement learning, fine-grained supervision, and architectural decoupling to explicitly repair these deviations. Although these interventions improve classification accuracy and absolute scores, the relative ranking gap persists. These findings suggest that, within the pointwise scoring paradigm, routing continuous relevance semantics through discrete text constrains ranking signal resolution, revealing a bottleneck that is stable and difficult to overcome under current standard methods, rather than an easily resolvable training bias.
%R 10.18653/2026.findings-emnlp.987
%U https://aclanthology.org/2026.findings-emnlp.987/
%U https://doi.org/10.18653/2026.findings-emnlp.987
%P 20854-20865
Markdown (Informal)
[Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking](https://aclanthology.org/2026.findings-emnlp.987/) (Chen et al., Findings 2026)
ACL
- Xiaoyang Chen, Jie Liu, Haijin Liang, Haibo Shi, Jin Ma, Ben He, Yingfei Sun, and Dezhi Ye. 2026. Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20854–20865, Budapest, Hungary. Association for Computational Linguistics.