@inproceedings{zhang-etal-2026-qubric,
title = "{QUBRIC}: Co-Designing Queries and Rubrics for {RL} Beyond Verifiable Rewards",
author = "Zhang, Rongzhi and
Feng, Rui and
Zhang, Zhihan and
Yang, Jingfeng and
Yin, Qingyu and
Liu, Xin and
Zhang, Zixuan and
Nigam, Priyanka and
Yin, Bing and
Zhao, Tuo and
Zhang, Chao",
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.728/",
pages = "16914--16931",
ISBN = "979-8-89176-459-0",
abstract = "Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as fixed. We identify a structural bottleneck: rubric quality is constrained by query structure. Open-ended queries yield vague rubrics; naively narrowing them introduces fabricated references that no model can verify, so all responses fail and training receives no reward signal. We present QUBRIC, a framework that co-designs queries and rubrics. Teacher-derived key points ground the rewriting of open-ended prompts into scenario-based, evaluable questions. Contrastive rubric generation then turns teacher{--}policy gaps into query-level criteria, and learnability filtering retains only informative query{--}rubric pairs for GRPO training. QUBRIC achieves a +5.5 point gain on ArenaHard over the SFT baseline. Trained only on instruction-following data, it further transfers to three held-out benchmarks spanning legal, moral, and narrative reasoning (+6.3 points on average), with improvements concentrated in reasoning-related dimensions. These results provide evidence that co-designing queries and rubrics can make rubric-based RL a practical complement to RLVR beyond strictly verifiable tasks."
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<abstract>Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as fixed. We identify a structural bottleneck: rubric quality is constrained by query structure. Open-ended queries yield vague rubrics; naively narrowing them introduces fabricated references that no model can verify, so all responses fail and training receives no reward signal. We present QUBRIC, a framework that co-designs queries and rubrics. Teacher-derived key points ground the rewriting of open-ended prompts into scenario-based, evaluable questions. Contrastive rubric generation then turns teacher–policy gaps into query-level criteria, and learnability filtering retains only informative query–rubric pairs for GRPO training. QUBRIC achieves a +5.5 point gain on ArenaHard over the SFT baseline. Trained only on instruction-following data, it further transfers to three held-out benchmarks spanning legal, moral, and narrative reasoning (+6.3 points on average), with improvements concentrated in reasoning-related dimensions. These results provide evidence that co-designing queries and rubrics can make rubric-based RL a practical complement to RLVR beyond strictly verifiable tasks.</abstract>
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%0 Conference Proceedings
%T QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards
%A Zhang, Rongzhi
%A Feng, Rui
%A Zhang, Zhihan
%A Yang, Jingfeng
%A Yin, Qingyu
%A Liu, Xin
%A Zhang, Zixuan
%A Nigam, Priyanka
%A Yin, Bing
%A Zhao, Tuo
%A Zhang, Chao
%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 zhang-etal-2026-qubric
%X Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as fixed. We identify a structural bottleneck: rubric quality is constrained by query structure. Open-ended queries yield vague rubrics; naively narrowing them introduces fabricated references that no model can verify, so all responses fail and training receives no reward signal. We present QUBRIC, a framework that co-designs queries and rubrics. Teacher-derived key points ground the rewriting of open-ended prompts into scenario-based, evaluable questions. Contrastive rubric generation then turns teacher–policy gaps into query-level criteria, and learnability filtering retains only informative query–rubric pairs for GRPO training. QUBRIC achieves a +5.5 point gain on ArenaHard over the SFT baseline. Trained only on instruction-following data, it further transfers to three held-out benchmarks spanning legal, moral, and narrative reasoning (+6.3 points on average), with improvements concentrated in reasoning-related dimensions. These results provide evidence that co-designing queries and rubrics can make rubric-based RL a practical complement to RLVR beyond strictly verifiable tasks.
%U https://aclanthology.org/2026.emnlp-main.728/
%P 16914-16931
Markdown (Informal)
[QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards](https://aclanthology.org/2026.emnlp-main.728/) (Zhang et al., EMNLP 2026)
ACL
- Rongzhi Zhang, Rui Feng, Zhihan Zhang, Jingfeng Yang, Qingyu Yin, Xin Liu, Zixuan Zhang, Priyanka Nigam, Bing Yin, Tuo Zhao, and Chao Zhang. 2026. QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 16914–16931, Budapest, Hungary. Association for Computational Linguistics.