@inproceedings{hu-etal-2026-ce,
title = "{CE}-{RM}: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria",
author = "Hu, Xinyu and
He, Yancheng and
Wang, Weixun and
Feng, Tao and
Lin, Li and
Liu, Jiashun and
Su, Wenbo and
Zheng, Bo and
Wan, Xiaojun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.982/",
pages = "19629--19642",
ISBN = "979-8-89176-395-1",
abstract = "Automatic evaluation is crucial yet challenging for open-ended natural language generation, especially when rule-based metrics are infeasible. Compared with traditional methods, the recent LLM-as-a-Judge paradigms enable better and more flexible evaluation, and show promise as generative reward models for reinforcement learning. However, prior work has revealed a notable gap between their seemingly impressive benchmark performance and actual effectiveness in RL practice. We attribute this issue to some limitations in existing studies, including the dominance of pairwise evaluation and inadequate optimization of evaluation criteria. Therefore, we propose **CE-RM-4B**, a pointwise generative reward model trained with a dedicated two-stage rollout method, and adopting unified query-based criteria. Using only about 5.7K high-quality data curated from the open-source preference dataset, our CE-RM-4B achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice."
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<abstract>Automatic evaluation is crucial yet challenging for open-ended natural language generation, especially when rule-based metrics are infeasible. Compared with traditional methods, the recent LLM-as-a-Judge paradigms enable better and more flexible evaluation, and show promise as generative reward models for reinforcement learning. However, prior work has revealed a notable gap between their seemingly impressive benchmark performance and actual effectiveness in RL practice. We attribute this issue to some limitations in existing studies, including the dominance of pairwise evaluation and inadequate optimization of evaluation criteria. Therefore, we propose **CE-RM-4B**, a pointwise generative reward model trained with a dedicated two-stage rollout method, and adopting unified query-based criteria. Using only about 5.7K high-quality data curated from the open-source preference dataset, our CE-RM-4B achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice.</abstract>
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%0 Conference Proceedings
%T CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria
%A Hu, Xinyu
%A He, Yancheng
%A Wang, Weixun
%A Feng, Tao
%A Lin, Li
%A Liu, Jiashun
%A Su, Wenbo
%A Zheng, Bo
%A Wan, Xiaojun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hu-etal-2026-ce
%X Automatic evaluation is crucial yet challenging for open-ended natural language generation, especially when rule-based metrics are infeasible. Compared with traditional methods, the recent LLM-as-a-Judge paradigms enable better and more flexible evaluation, and show promise as generative reward models for reinforcement learning. However, prior work has revealed a notable gap between their seemingly impressive benchmark performance and actual effectiveness in RL practice. We attribute this issue to some limitations in existing studies, including the dominance of pairwise evaluation and inadequate optimization of evaluation criteria. Therefore, we propose **CE-RM-4B**, a pointwise generative reward model trained with a dedicated two-stage rollout method, and adopting unified query-based criteria. Using only about 5.7K high-quality data curated from the open-source preference dataset, our CE-RM-4B achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice.
%U https://aclanthology.org/2026.findings-acl.982/
%P 19629-19642
Markdown (Informal)
[CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria](https://aclanthology.org/2026.findings-acl.982/) (Hu et al., Findings 2026)
ACL
- Xinyu Hu, Yancheng He, Weixun Wang, Tao Feng, Li Lin, Jiashun Liu, Wenbo Su, Bo Zheng, and Xiaojun Wan. 2026. CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19629–19642, San Diego, California, United States. Association for Computational Linguistics.