@inproceedings{zeng-etal-2026-tinyjudge,
title = "{T}iny{J}udge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles",
author = "Zeng, Yirong and
Liu, Yufei and
Ding, Xiao and
Hou, Yutai and
Wang, Yuxian and
Ning, Wu and
Song, Haonan and
Tu, Dandan and
Zhang, Qixun and
He, Yuxiang and
Cai, Bibo and
Liu, Ting",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1204/",
pages = "26198--26212",
ISBN = "979-8-89176-390-6",
abstract = "Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output length) to unverifiable ones (e.g., tone). Reinforcement learning with verifiable rewards has emerged as a paradigm for IF tasks, leveraging LLM-as-a-judge to assess unverifiable constraints. However, we empirically find that this approach remains a significant bottleneck, suffering from severe reward hacking and higher computational overhead. In this work, we first analyze the generalization capabilities of unverifiable constraints and discover that specific constraints exhibit distinct, high-generalization patterns. Motivated by this, we propose TinyJudge, a framework that employs an ensemble of specialized tiny language models (e.g., 0.6B) to provide rewards for soft constraints. By distilling expertise from frontier models into these tiny models, it achieves high-precision, lightweight evaluation. Extensive evaluations across five benchmarks demonstrate that TinyJudge outperforms the baselines by {\textasciitilde}10{\%} in average performance and 12{\%} in reward precision. Crucially, it also achieves a 3{\texttimes} speedup in total training time. Our work provides a scalable and robust path for aligning LLMs with unverifiable human instructions."
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<abstract>Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output length) to unverifiable ones (e.g., tone). Reinforcement learning with verifiable rewards has emerged as a paradigm for IF tasks, leveraging LLM-as-a-judge to assess unverifiable constraints. However, we empirically find that this approach remains a significant bottleneck, suffering from severe reward hacking and higher computational overhead. In this work, we first analyze the generalization capabilities of unverifiable constraints and discover that specific constraints exhibit distinct, high-generalization patterns. Motivated by this, we propose TinyJudge, a framework that employs an ensemble of specialized tiny language models (e.g., 0.6B) to provide rewards for soft constraints. By distilling expertise from frontier models into these tiny models, it achieves high-precision, lightweight evaluation. Extensive evaluations across five benchmarks demonstrate that TinyJudge outperforms the baselines by ~10% in average performance and 12% in reward precision. Crucially, it also achieves a 3× speedup in total training time. Our work provides a scalable and robust path for aligning LLMs with unverifiable human instructions.</abstract>
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%0 Conference Proceedings
%T TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles
%A Zeng, Yirong
%A Liu, Yufei
%A Ding, Xiao
%A Hou, Yutai
%A Wang, Yuxian
%A Ning, Wu
%A Song, Haonan
%A Tu, Dandan
%A Zhang, Qixun
%A He, Yuxiang
%A Cai, Bibo
%A Liu, Ting
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zeng-etal-2026-tinyjudge
%X Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output length) to unverifiable ones (e.g., tone). Reinforcement learning with verifiable rewards has emerged as a paradigm for IF tasks, leveraging LLM-as-a-judge to assess unverifiable constraints. However, we empirically find that this approach remains a significant bottleneck, suffering from severe reward hacking and higher computational overhead. In this work, we first analyze the generalization capabilities of unverifiable constraints and discover that specific constraints exhibit distinct, high-generalization patterns. Motivated by this, we propose TinyJudge, a framework that employs an ensemble of specialized tiny language models (e.g., 0.6B) to provide rewards for soft constraints. By distilling expertise from frontier models into these tiny models, it achieves high-precision, lightweight evaluation. Extensive evaluations across five benchmarks demonstrate that TinyJudge outperforms the baselines by ~10% in average performance and 12% in reward precision. Crucially, it also achieves a 3× speedup in total training time. Our work provides a scalable and robust path for aligning LLMs with unverifiable human instructions.
%U https://aclanthology.org/2026.acl-long.1204/
%P 26198-26212
Markdown (Informal)
[TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles](https://aclanthology.org/2026.acl-long.1204/) (Zeng et al., ACL 2026)
ACL
- Yirong Zeng, Yufei Liu, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Qixun Zhang, Yuxiang He, Bibo Cai, and Ting Liu. 2026. TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 26198–26212, San Diego, California, United States. Association for Computational Linguistics.