--- title: "Who cites Generative Reward Models — 58 papers of our library" description: "58 papers of the Elamur Research library cite «Generative Reward Models», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Generative Reward Models](https://md.research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/) / cited by # Who cites «Generative Reward Models» 58 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← back to «Generative Reward Models»](https://md.research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/) 1. [**Seed1.5-VL Technical Report arXiv:2505.07062**Dong Guo, Faming Wu, Feida Zhu, et al.*2025 · 67 citations*](https://md.research.elamur.ai/free/en/papers/guo-2025-seed1-5-vl-technical/) 2. [**Inference-Time Scaling for Generalist Reward Modeling arXiv:2504.02495**Zijun Liu, Peiyi Wang, Runxin Xu, et al.*2025 · 65 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-inference-time-scaling-generalist/) 3. [**Free Process Rewards without Process Labels arXiv:2412.01981**Lifan Yuan, Wendi Li, Huayu Chen, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-free-process-rewards-process-labels/) 4. [**AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset arXiv:2504.16891**Ivan Moshkov, Darragh Hanley, Ivan Sorokin, et al.*2025 · 50 citations*](https://md.research.elamur.ai/free/en/papers/moshkov-2025-aimo-2-winning-solution-building/) 5. [**Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought arXiv:2501.04682**Violet Xiang, Charlie Snell, Kanishk Gandhi, et al.*2025 · 47 citations*](https://md.research.elamur.ai/free/en/papers/xiang-2025-towards-system-2-reasoning-llms/) 6. [**Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models arXiv:2502.17387**Alon Albalak, Duy Phung, Nathan Lile, et al.*2025 · 43 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/) 7. [**A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems arXiv:2504.09037**Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ke-2025-survey-frontiers-llm-reasoning-inference/) 8. [**Scaling Test-Time Compute Without Verification or RL is Suboptimal arXiv:2502.12118**Amrith Setlur, Nived Rajaraman, Sergey Levine, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/setlur-2025-scaling-test-time-compute-verification/) 9. [**Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models arXiv:2412.02980**Alex Havrilla, Andrew Dai, Laura O'Mahony, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-surveying-effects-quality-diversity-complexity/) 10. **[Improve LLM-as-a-Judge Ability as a General Ability](https://md.research.elamur.ai/free/en/papers/yu-2025-improve-llm-as-judge-ability/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiachen Yu, Shaoning Sun, Xiaohui Hu, et al.*2025 · 15 citations* 11. [**When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning arXiv:2504.01005**Nishad Singhi, Hritik Bansal, Arian Hosseini, et al.*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/singhi-2025-solve-verify-compute-optimal-problem/) 12. [**Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought arXiv:2505.15431**Tencent Hunyuan Team, Ao Liu, Botong Zhou, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-hunyuan-hunyuan-turbos-advancing-large/) 13. **[Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty](https://md.research.elamur.ai/free/en/papers/xue-2026-reason-only-when-needed-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chao Xue, Yao Wang, Mengqiao Liu, et al.*2026 · 8 citations* 14. [**PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier arXiv:2506.10406**Yuhua Jiang, Yuwen Xiong, Yufeng Yuan, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2025-pag-multi-turn-reinforced-llm/) 15. **[J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization](https://md.research.elamur.ai/free/en/papers/xu-2026-j4r-learning-judge-equivalent-initial/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Austin Xu, Yilun Zhou, Xuan-Phi Nguyen, et al.*2026 · 3 citations* 16. **[Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math](https://md.research.elamur.ai/free/en/papers/pandit-2026-hard2verify-step-level-verification-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shrey Pandit, Austin Xu, Xuan-Phi Nguyen, et al.*2026 · 2 citations* 17. [**Hunyuan-A13B Technical Report arXiv:2609.27284**Tencent Hunyuan Team, Ao Liu, Botong Zhou, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/team-2026-hunyuan-hunyuan-a13b-technical-report/) 18. **[ReportLogic: Evaluating Logical Quality in Deep Research Reports](https://md.research.elamur.ai/free/en/papers/zhao-2026-reportlogic-evaluating-logical-quality-deep/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jujia Zhao, Zhaoxin Huan, Zihan Wang, et al.*2026 · 1 citation* 19. **[Scaling Test-Time Compute to Achieve IOI Gold Medal with Open-Weight Models](https://md.research.elamur.ai/free/en/papers/samadi-2026-scaling-test-time-compute-achieve/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehrzad Samadi, Aleksander Ficek, Sean Narenthiran, et al.*2026 · 1 citation* 20. **[UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wei-2026-unicreative-unifying-long-form-logic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaolong Wei, Zerun Zhu, Simin Niu, et al.*2026 · 1 citation* 21. **[AgentV-RL: Scaling Reward Modeling with Agentic Verifier](https://md.research.elamur.ai/free/en/papers/zhang-2026-agentv-rl-scaling-reward-modeling/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiazheng Zhang, Ziche Fu, Zhiheng Xi, et al.*2026* 22. **[Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-learning-refine-self-refinement-parallel/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qibin Wang, Pu Zhao, Shaohan Huang, et al.*2026* 23. **[PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering](https://md.research.elamur.ai/free/en/papers/wang-2026-prime-process-outcome-alignment-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiangfeng Wang, Hangyu Guo, Yanlin Lai, et al.*2026* 24. **[ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework](https://md.research.elamur.ai/free/en/papers/qin-2026-reflectrm-boosting-generative-reward-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kai Qin, Liangxin Liu, Yu Liang, et al.*2026* 34 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/) — Generative Reward Models