🔗 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»

1. s1: Simple test-time scalingConference on Empirical Methods in Natural Language Processing (EMNLP)Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.2025 · 608 citations
2. Kimi k1.5: Scaling Reinforcement Learning with LLMsarXiv:2501.12599Kimi Team, Angang Du, Bofei Gao, et al.2025 · 431 citations
3. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
4. Generative Verifiers: Reward Modeling as Next-Token PredictionarXiv:2408.15240Lunjun Zhang, Arian Hosseini, Hritik Bansal, et al.2024 · 152 citations
5. Inference-Time Scaling for Generalist Reward ModelingarXiv:2504.02495Zijun Liu, Peiyi Wang, Runxin Xu, et al.2025 · 65 citations
6. Generative Reward ModelsarXiv:2410.12832Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.2024 · 58 citations
7. AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning datasetarXiv:2504.16891Ivan Moshkov, Darragh Hanley, Ivan Sorokin, et al.2025 · 50 citations
8. RMB: Comprehensively Benchmarking Reward Models in LLM AlignmentarXiv:2410.09893Enyu Zhou, Guodong Zheng, Binghai Wang, et al.2024 · 30 citations
9. TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and GenerationarXiv:2410.03608Jonathan Cook, Tim Rocktaschel, Jakob Foerster, et al.2024 · 29 citations
10. A Survey on Large Language Models for Mathematical ReasoningACM Computing SurveysPeng-Yuan Wang, Tian-Shuo Liu, Chenyang Wang, et al.2025 · 26 citations
11. OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM AlignmentAnnual Meeting of the Association for Computational Linguistics (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-GenerationarXiv:2410.02725Rohin Manvi, Anikait Singh, Stefano Ermon2024 · 22 citations
13. Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language ModelsarXiv:2412.02980Alex Havrilla, Andrew Dai, Laura O'Mahony, et al.2024 · 17 citations
14. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language ModelsarXiv:2503.24377Rui Wang, Hongru Wang, Boyang Xue, et al.2025 · 16 citations
15. When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM ReasoningarXiv:2504.01005Nishad Singhi, Hritik Bansal, Arian Hosseini, et al.2025 · 12 citations
16. mR3: Multilingual Rubric-Agnostic Reward Reasoning ModelsarXiv:2510.01146David Anugraha, Shou-Yi Hung, Zilu Tang, et al.2025 · 5 citations
17. DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree TraversalAnnual Meeting of the Association for Computational Linguistics (ACL)Vaibhav Aggarwal, Ojasv Kamal, Abhinav Japesh, et al.2025 · 4 citations
18. Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward ModelsFindings of the Association for Computational LinguisticsQiyuan Zhang, Yufei Wang, Tianhe Wu, et al.2026
19. JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM EvaluationFindings of the Association for Computational LinguisticsZhichao Shi, Xuhui Jiang, Chengjin Xu, et al.2026
20. PIRA: Preference-Oriented Instruction-Tuned Reward Models with Dual AggregationFindings of the Association for Computational LinguisticsYongfu Xue2026
21. Reward Modeling for Scientific Writing EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Furkan Şahinuç, Subhabrata Dutta, Iryna Gurevych2026

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

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/cited-by/