@inproceedings{yu-etal-2026-reinforcement,
title = "Reinforcement Learning with Robust Rubric Rewards",
author = "Yu, Ya-Qi and
Wang, Hao and
Hong, Fangyu and
Qu, Xiangyang and
Wu, Gaojie and
Luo, Qiaoyu and
Xu, Nuo and
Wang, Huixin and
Xu, Wuheng and
Liao, Yongxin and
Chen, Zihao and
Li, Haonan and
Li, Ziming and
Peng, Dezhi and
Liao, Minghui and
Wu, Jihao and
Ren, Haoyu and
Tu, Dandan",
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.2554/",
pages = "58060--58078",
ISBN = "979-8-89176-459-0",
abstract = "While RLVR is effective for deterministically verifiable tasks, many vision-language tasks are only *partially verifiable*, demanding multi-criterion supervision. To bridge this gap, we propose *Reinforcement Learning with Robust Rubric Rewards ($\text{RLR}^3$), extending RL with criterion-level rewards. To ensure faithful scoring, $\text{RLR}^3$ routes each criterion to one of two execution paths: an LLM-as-an-extractor paired with a verifier, or an LLM-as-a-Judge for fuzzy criteria. We also introduce a minimal exposure strategy that masks verifier targets from extractors and source images from the GenRM. Furthermore, $\text{RLR}^3$ employs decoupled normalization to mitigate score saturation and hierarchical aggregation to prioritize essential criteria. Evaluated on Qwen3-VL-30B-A3B across 15 benchmarks, $\text{RLR}^3$ consistently outperforms RLVR, yielding a 4.7-point improvement over the base model and exceeding the official instruct-to-thinking model gap. Controlled audits show that deterministic verifier and minimal exposure strategy reduce exploitable false positives."
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<abstract>While RLVR is effective for deterministically verifiable tasks, many vision-language tasks are only *partially verifiable*, demanding multi-criterion supervision. To bridge this gap, we propose *Reinforcement Learning with Robust Rubric Rewards (\textRLR³), extending RL with criterion-level rewards. To ensure faithful scoring, \textRLR³ routes each criterion to one of two execution paths: an LLM-as-an-extractor paired with a verifier, or an LLM-as-a-Judge for fuzzy criteria. We also introduce a minimal exposure strategy that masks verifier targets from extractors and source images from the GenRM. Furthermore, \textRLR³ employs decoupled normalization to mitigate score saturation and hierarchical aggregation to prioritize essential criteria. Evaluated on Qwen3-VL-30B-A3B across 15 benchmarks, \textRLR³ consistently outperforms RLVR, yielding a 4.7-point improvement over the base model and exceeding the official instruct-to-thinking model gap. Controlled audits show that deterministic verifier and minimal exposure strategy reduce exploitable false positives.</abstract>
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%0 Conference Proceedings
%T Reinforcement Learning with Robust Rubric Rewards
%A Yu, Ya-Qi
%A Wang, Hao
%A Hong, Fangyu
%A Qu, Xiangyang
%A Wu, Gaojie
%A Luo, Qiaoyu
%A Xu, Nuo
%A Wang, Huixin
%A Xu, Wuheng
%A Liao, Yongxin
%A Chen, Zihao
%A Li, Haonan
%A Li, Ziming
%A Peng, Dezhi
%A Liao, Minghui
%A Wu, Jihao
%A Ren, Haoyu
%A Tu, Dandan
%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 yu-etal-2026-reinforcement
%X While RLVR is effective for deterministically verifiable tasks, many vision-language tasks are only *partially verifiable*, demanding multi-criterion supervision. To bridge this gap, we propose *Reinforcement Learning with Robust Rubric Rewards (\textRLR³), extending RL with criterion-level rewards. To ensure faithful scoring, \textRLR³ routes each criterion to one of two execution paths: an LLM-as-an-extractor paired with a verifier, or an LLM-as-a-Judge for fuzzy criteria. We also introduce a minimal exposure strategy that masks verifier targets from extractors and source images from the GenRM. Furthermore, \textRLR³ employs decoupled normalization to mitigate score saturation and hierarchical aggregation to prioritize essential criteria. Evaluated on Qwen3-VL-30B-A3B across 15 benchmarks, \textRLR³ consistently outperforms RLVR, yielding a 4.7-point improvement over the base model and exceeding the official instruct-to-thinking model gap. Controlled audits show that deterministic verifier and minimal exposure strategy reduce exploitable false positives.
%U https://aclanthology.org/2026.emnlp-main.2554/
%P 58060-58078
Markdown (Informal)
[Reinforcement Learning with Robust Rubric Rewards](https://aclanthology.org/2026.emnlp-main.2554/) (Yu et al., EMNLP 2026)
ACL
- Ya-Qi Yu, Hao Wang, Fangyu Hong, Xiangyang Qu, Gaojie Wu, Qiaoyu Luo, Nuo Xu, Huixin Wang, Wuheng Xu, Yongxin Liao, Zihao Chen, Haonan Li, Ziming Li, Dezhi Peng, Minghui Liao, Jihao Wu, Haoyu Ren, and Dandan Tu. 2026. Reinforcement Learning with Robust Rubric Rewards. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 58060–58078, Budapest, Hungary. Association for Computational Linguistics.