@inproceedings{ye-etal-2026-rethinking,
title = "Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective",
author = "Ye, Shenghao and
Wang, Yuxiang and
Guo, Yu and
Zhang, Tao and
Chen, Shuangwu and
Wen, Zijian and
Zhu, Qihui and
Zhang, Mengjie and
Jin, Dong and
Luo, Wang and
Jianyang",
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.1232/",
pages = "28102--28120",
ISBN = "979-8-89176-459-0",
abstract = "Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Stepwise model routing mitigates this issue by dynamically assigning reasoning steps to smaller or larger models. However, stepwise model routing for table reasoning remains underexplored. Through empirical analysis, we find that reasoning steps involving tables contain two types of tokens with distinct uncertainty distributions: table tokens grounded in table structure, such as cell values and headers, and text tokens representing surrounding natural-language reasoning. The uncertainty of both token types is correlated with the risk that the model makes an error in the next reasoning step. However, existing methods fail to model them separately, leading to suboptimal routing decisions. To address this, we propose EcoTab, a table-aware stepwise routing framework for efficient table reasoning. For each current reasoning step, EcoTab separately models the two types of tokens and estimates their uncertainties, then combines them to estimate the risk of using the small model for the next step, thereby enabling model selection. Experiments on multiple table reasoning benchmarks show that EcoTab consistently outperforms strong baselines and achieves a better balance between accuracy and efficiency."
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<abstract>Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Stepwise model routing mitigates this issue by dynamically assigning reasoning steps to smaller or larger models. However, stepwise model routing for table reasoning remains underexplored. Through empirical analysis, we find that reasoning steps involving tables contain two types of tokens with distinct uncertainty distributions: table tokens grounded in table structure, such as cell values and headers, and text tokens representing surrounding natural-language reasoning. The uncertainty of both token types is correlated with the risk that the model makes an error in the next reasoning step. However, existing methods fail to model them separately, leading to suboptimal routing decisions. To address this, we propose EcoTab, a table-aware stepwise routing framework for efficient table reasoning. For each current reasoning step, EcoTab separately models the two types of tokens and estimates their uncertainties, then combines them to estimate the risk of using the small model for the next step, thereby enabling model selection. Experiments on multiple table reasoning benchmarks show that EcoTab consistently outperforms strong baselines and achieves a better balance between accuracy and efficiency.</abstract>
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%0 Conference Proceedings
%T Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective
%A Ye, Shenghao
%A Wang, Yuxiang
%A Guo, Yu
%A Zhang, Tao
%A Chen, Shuangwu
%A Wen, Zijian
%A Zhu, Qihui
%A Zhang, Mengjie
%A Jin, Dong
%A Luo, Wang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Jianyang
%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 ye-etal-2026-rethinking
%X Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Stepwise model routing mitigates this issue by dynamically assigning reasoning steps to smaller or larger models. However, stepwise model routing for table reasoning remains underexplored. Through empirical analysis, we find that reasoning steps involving tables contain two types of tokens with distinct uncertainty distributions: table tokens grounded in table structure, such as cell values and headers, and text tokens representing surrounding natural-language reasoning. The uncertainty of both token types is correlated with the risk that the model makes an error in the next reasoning step. However, existing methods fail to model them separately, leading to suboptimal routing decisions. To address this, we propose EcoTab, a table-aware stepwise routing framework for efficient table reasoning. For each current reasoning step, EcoTab separately models the two types of tokens and estimates their uncertainties, then combines them to estimate the risk of using the small model for the next step, thereby enabling model selection. Experiments on multiple table reasoning benchmarks show that EcoTab consistently outperforms strong baselines and achieves a better balance between accuracy and efficiency.
%U https://aclanthology.org/2026.emnlp-main.1232/
%P 28102-28120
Markdown (Informal)
[Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective](https://aclanthology.org/2026.emnlp-main.1232/) (Ye et al., EMNLP 2026)
ACL
- Shenghao Ye, Yuxiang Wang, Yu Guo, Tao Zhang, Shuangwu Chen, Zijian Wen, Qihui Zhu, Mengjie Zhang, Dong Jin, Wang Luo, and Jianyang. 2026. Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 28102–28120, Budapest, Hungary. Association for Computational Linguistics.