@inproceedings{ye-etal-2026-rubric,
title = "Rubric-Guided Process Reward for Stepwise Model Routing",
author = "Ye, Shenghao and
Guo, Yu and
Li, Zhengheng and
Huang, Qiang and
Chen, Shuangwu and
Luo, Wang and
Qian, Rui and
Zhang, Jie and
Zhang, Tao and
Jin, Dong and
Tan, Xiaobin and
Zheng, Quan 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.1222/",
pages = "27892--27910",
ISBN = "979-8-89176-459-0",
abstract = "Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequential decision process and train the router with reinforcement learning. However, although they model routing as a process, they still supervise the router with outcome rewards. Such rewards only reflect final answer correctness and fail to evaluate intermediate routing decisions, which can weaken performance and generalization. To address this gap, we propose RoRo, a rubric-guided process reward framework for stepwise model routing. RoRo first collects diverse routing trajectories and constructs preference pairs based on outcome, cost, and process quality. It then trains a Rubricor to generate a query-specific evaluation rubric and a Judge to score routing trajectories under this rubric through alternating optimization. The resulting process rewards are combined with outcome rewards to optimize the routing policy via GRPO. Experiments on five reasoning benchmarks under both same-family and cross-family settings show that RoRo consistently outperforms strong baselines and achieves better accuracy and cost trade-offs."
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<abstract>Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequential decision process and train the router with reinforcement learning. However, although they model routing as a process, they still supervise the router with outcome rewards. Such rewards only reflect final answer correctness and fail to evaluate intermediate routing decisions, which can weaken performance and generalization. To address this gap, we propose RoRo, a rubric-guided process reward framework for stepwise model routing. RoRo first collects diverse routing trajectories and constructs preference pairs based on outcome, cost, and process quality. It then trains a Rubricor to generate a query-specific evaluation rubric and a Judge to score routing trajectories under this rubric through alternating optimization. The resulting process rewards are combined with outcome rewards to optimize the routing policy via GRPO. Experiments on five reasoning benchmarks under both same-family and cross-family settings show that RoRo consistently outperforms strong baselines and achieves better accuracy and cost trade-offs.</abstract>
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%0 Conference Proceedings
%T Rubric-Guided Process Reward for Stepwise Model Routing
%A Ye, Shenghao
%A Guo, Yu
%A Li, Zhengheng
%A Huang, Qiang
%A Chen, Shuangwu
%A Luo, Wang
%A Qian, Rui
%A Zhang, Jie
%A Zhang, Tao
%A Jin, Dong
%A Tan, Xiaobin
%A Zheng, Quan
%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-rubric
%X Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequential decision process and train the router with reinforcement learning. However, although they model routing as a process, they still supervise the router with outcome rewards. Such rewards only reflect final answer correctness and fail to evaluate intermediate routing decisions, which can weaken performance and generalization. To address this gap, we propose RoRo, a rubric-guided process reward framework for stepwise model routing. RoRo first collects diverse routing trajectories and constructs preference pairs based on outcome, cost, and process quality. It then trains a Rubricor to generate a query-specific evaluation rubric and a Judge to score routing trajectories under this rubric through alternating optimization. The resulting process rewards are combined with outcome rewards to optimize the routing policy via GRPO. Experiments on five reasoning benchmarks under both same-family and cross-family settings show that RoRo consistently outperforms strong baselines and achieves better accuracy and cost trade-offs.
%U https://aclanthology.org/2026.emnlp-main.1222/
%P 27892-27910
Markdown (Informal)
[Rubric-Guided Process Reward for Stepwise Model Routing](https://aclanthology.org/2026.emnlp-main.1222/) (Ye et al., EMNLP 2026)
ACL
- Shenghao Ye, Yu Guo, Zhengheng Li, Qiang Huang, Shuangwu Chen, Wang Luo, Rui Qian, Jie Zhang, Tao Zhang, Dong Jin, Xiaobin Tan, Quan Zheng, and Jianyang. 2026. Rubric-Guided Process Reward for Stepwise Model Routing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 27892–27910, Budapest, Hungary. Association for Computational Linguistics.