--- title: "Who cites Critique-out-Loud Reward Models — 51 citing papers" description: "51 papers of the Elamur Research library cite «Critique-out-Loud Reward Models», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Critique-out-Loud Reward Models](https://md.research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/) / cited by # Who cites «Critique-out-Loud Reward Models» 51 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← back to «Critique-out-Loud Reward Models»](https://md.research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/) 1. **[s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 608 citations* 2. [**Kimi k1.5: Scaling Reinforcement Learning with LLMs arXiv:2501.12599**Kimi Team, Angang Du, Bofei Gao, et al.*2025 · 431 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/) 3. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations* 4. [**Generative Verifiers: Reward Modeling as Next-Token Prediction arXiv:2408.15240**Lunjun Zhang, Arian Hosseini, Hritik Bansal, et al.*2024 · 152 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-generative-verifiers-reward-modeling-as/) 5. [**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/) 6. [**Generative Reward Models arXiv:2410.12832**Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/) 7. [**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/) 8. [**RMB: Comprehensively Benchmarking Reward Models in LLM Alignment arXiv:2410.09893**Enyu Zhou, Guodong Zheng, Binghai Wang, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-rmb-comprehensively-benchmarking-reward-models/) 9. [**TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation arXiv:2410.03608**Jonathan Cook, Tim Rocktaschel, Jakob Foerster, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/cook-2024-ticking-all-boxes-generated-checklists/) 10. **[A Survey on Large Language Models for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-survey-large-language-models-mathematical/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Peng-Yuan Wang, Tian-Shuo Liu, Chenyang Wang, et al.*2025 · 26 citations* 11. **[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/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianci Liu, Ran Xu, Tony Yu, et al.*2026 · 25 citations* 12. [**Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation arXiv:2410.02725**Rohin Manvi, Anikait Singh, Stefano Ermon*2024 · 22 citations*](https://md.research.elamur.ai/free/en/papers/manvi-2024-adaptive-inference-time-compute-llms/) 13. [**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/) 14. [**Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models arXiv:2503.24377**Rui Wang, Hongru Wang, Boyang Xue, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-harnessing-reasoning-economy-survey-efficient/) 15. [**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/) 16. [**mR3: Multilingual Rubric-Agnostic Reward Reasoning Models arXiv:2510.01146**David Anugraha, Shou-Yi Hung, Zilu Tang, et al.*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/anugraha-2025-mr3-multilingual-rubric-agnostic-reward/) 17. **[DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal](https://md.research.elamur.ai/free/en/papers/aggarwal-2025-dars-dynamic-action-re-sampling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vaibhav Aggarwal, Ojasv Kamal, Abhinav Japesh, et al.*2025 · 4 citations* 18. **[Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-length-scaling-synergizing-breadth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiyuan Zhang, Yufei Wang, Tianhe Wu, et al.*2026* 19. **[JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation](https://md.research.elamur.ai/free/en/papers/shi-2026-judgeagent-beyond-static-benchmarks-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichao Shi, Xuhui Jiang, Chengjin Xu, et al.*2026* 20. **[PIRA: Preference-Oriented Instruction-Tuned Reward Models with Dual Aggregation](https://md.research.elamur.ai/free/en/papers/xue-2026-pira-preference-oriented-instruction-tuned/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yongfu Xue*2026* 21. **[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* 30 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/) — Critique-out-Loud Reward Models