---
title: "Who cites OpenRubrics: Towards Scalable — 23 citing papers"
description: "23 papers of the Elamur Research library cite «OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM…"
canonical: "https://research.elamur.ai/free/en/papers/liu-2026-openrubrics-scalable-synthetic-rubric-generation/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [OpenRubrics: Towards Scalable Synthetic Rubric…](https://md.research.elamur.ai/free/en/papers/liu-2026-openrubrics-scalable-synthetic-rubric-generation/) / cited by

# Who cites «OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment»

23 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment»](https://md.research.elamur.ai/free/en/papers/liu-2026-openrubrics-scalable-synthetic-rubric-generation/)

1. **[AttnPO: Attention-Guided Process Supervision for Efficient Reasoning](https://md.research.elamur.ai/free/en/papers/nie-2026-attnpo-attention-guided-process-supervision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuaiyi Nie, Dingsiyu, Wenyuan Zhang, et al.*2026 · 18 citations*
2. **[RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation](https://md.research.elamur.ai/free/en/papers/li-2026-rubrichub-comprehensive-highly-discriminative-rubric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sunzhu Li, Jiale Zhao, Huimin Ren, et al.*2026 · 9 citations*
3. **[RubricBench: Aligning Model-Generated Rubrics with Human Standards](https://md.research.elamur.ai/free/en/papers/zhou-2026-rubricbench-aligning-model-generated-rubrics/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyi Zhou, Qiyuan Zhang, Yufei Wang, et al.*2026 · 3 citations*
4. **[IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation](https://md.research.elamur.ai/free/en/papers/wen-2026-if-critic-fine-grained-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bosi Wen, Yilin Niu, Cunxiang Wang, et al.*2026 · 1 citation*
5. **[P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist](https://md.research.elamur.ai/free/en/papers/seo-2026-p-check-advancing-personalized-reward/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kwangwook Seo, Dongha Lee*2026 · 1 citation*
6. **[SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility](https://md.research.elamur.ai/free/en/papers/zhi-2026-spard-self-paced-curriculum-rl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuyang Zhi, Peilun Zhou, Chengqiang Lu, et al.*2026 · 1 citation*
7. **[Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines](https://md.research.elamur.ai/free/en/papers/song-2026-alignment-tuning-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hwanjun Song*2026*
8. **[Reward Modeling for Scientific Writing Evaluation](https://md.research.elamur.ai/free/en/papers/sahinuc-2026-reward-modeling-scientific-writing-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Furkan Şahinuç, Subhabrata Dutta, Iryna Gurevych*2026*
9. **[RiT: Rubrics-in-Thinking Reinforcement Learning for Improved Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/tian-2026-rit-rubrics-thinking-reinforcement-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaobin Tian, Shuai Yuan, Muyun Ding, et al.*2026*

14 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/liu-2026-openrubrics-scalable-synthetic-rubric-generation/) — OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment