@inproceedings{li-etal-2026-sear,
title = "{SEAR}: Self-Judging Agentic Reward Models",
author = "Li, Bingqian and
Hao, Chuan and
Li, Junyi and
Tao, Ran and
Yang, Jian and
Zhao, Xin",
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.893/",
pages = "20591--20603",
ISBN = "979-8-89176-459-0",
abstract = "Agentic reward models enhance large language model (LLM) evaluation by incorporating external tool-use capabilities. However, their training is heavily constrained by coarse-grained outcome supervision and the high cost of process-level annotations. To address these challenges, we propose SEAR (Self-judging agentic reward models), a novel training framework that enables agentic reward models to self-judge their own judging processes. SEAR introduces a dual-role adversarial co-evolution framework, where the model alternates between a judger and a self-judger within a single generation process: the judger is optimized to produce more rigorous reasoning trajectories that maximize self-judgment scores, while the self-judger learns to provide critical and discriminative evaluations. Moreover, SEAR further enables agentic RMs to self-evolve without relying on external human annotations. Extensive experiments demonstrate that SEAR consistently improves the reliability of agentic reward models across diverse benchmarks."
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<abstract>Agentic reward models enhance large language model (LLM) evaluation by incorporating external tool-use capabilities. However, their training is heavily constrained by coarse-grained outcome supervision and the high cost of process-level annotations. To address these challenges, we propose SEAR (Self-judging agentic reward models), a novel training framework that enables agentic reward models to self-judge their own judging processes. SEAR introduces a dual-role adversarial co-evolution framework, where the model alternates between a judger and a self-judger within a single generation process: the judger is optimized to produce more rigorous reasoning trajectories that maximize self-judgment scores, while the self-judger learns to provide critical and discriminative evaluations. Moreover, SEAR further enables agentic RMs to self-evolve without relying on external human annotations. Extensive experiments demonstrate that SEAR consistently improves the reliability of agentic reward models across diverse benchmarks.</abstract>
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%0 Conference Proceedings
%T SEAR: Self-Judging Agentic Reward Models
%A Li, Bingqian
%A Hao, Chuan
%A Li, Junyi
%A Tao, Ran
%A Yang, Jian
%A Zhao, Xin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%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 li-etal-2026-sear
%X Agentic reward models enhance large language model (LLM) evaluation by incorporating external tool-use capabilities. However, their training is heavily constrained by coarse-grained outcome supervision and the high cost of process-level annotations. To address these challenges, we propose SEAR (Self-judging agentic reward models), a novel training framework that enables agentic reward models to self-judge their own judging processes. SEAR introduces a dual-role adversarial co-evolution framework, where the model alternates between a judger and a self-judger within a single generation process: the judger is optimized to produce more rigorous reasoning trajectories that maximize self-judgment scores, while the self-judger learns to provide critical and discriminative evaluations. Moreover, SEAR further enables agentic RMs to self-evolve without relying on external human annotations. Extensive experiments demonstrate that SEAR consistently improves the reliability of agentic reward models across diverse benchmarks.
%U https://aclanthology.org/2026.emnlp-main.893/
%P 20591-20603
Markdown (Informal)
[SEAR: Self-Judging Agentic Reward Models](https://aclanthology.org/2026.emnlp-main.893/) (Li et al., EMNLP 2026)
ACL
- Bingqian Li, Chuan Hao, Junyi Li, Ran Tao, Jian Yang, and Xin Zhao. 2026. SEAR: Self-Judging Agentic Reward Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 20591–20603, Budapest, Hungary. Association for Computational Linguistics.