🔗 Who cites «RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold»

50 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · ← back to «RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold»

1. Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model ParametersarXiv:2408.03314Charlie Snell, Jaehoon Lee, Kelvin Xu, et al.2024 · 567 citations
2. 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
3. Generative Verifiers: Reward Modeling as Next-Token PredictionarXiv:2408.15240Lunjun Zhang, Arian Hosseini, Hritik Bansal, et al.2024 · 152 citations
4. Rewarding Progress: Scaling Automated Process Verifiers for LLM ReasoningarXiv:2410.08146Amrith Setlur, Chirag Nagpal, Adam Fisch, et al.2024 · 93 citations
5. Agent Q: Advanced Reasoning and Learning for Autonomous AI AgentsarXiv:2408.07199Pranav Putta, Edmund Mills, Naman Garg, et al.2024 · 68 citations
6. VinePPO: Refining Credit Assignment in RL Training of LLMsarXiv:2410.01679Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, et al.2024 · 63 citations
7. Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-ThoughtarXiv:2501.04682Violet Xiang, Charlie Snell, Kanishk Gandhi, et al.2025 · 47 citations
8. Improve Vision Language Model Chain-of-thought ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Ruohong Zhang, Bowen Zhang, Yanghao Li, et al.2025 · 42 citations
9. Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time ScalingarXiv:2502.06703Runze Liu, Junqi Gao, Jian Zhao, et al.2025 · 41 citations
10. Scaling Test-Time Compute Without Verification or RL is SuboptimalarXiv:2502.12118Amrith Setlur, Nived Rajaraman, Sergey Levine, et al.2025 · 26 citations
11. S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Ruotian Ma, Peisong Wang, Cheng Liu, et al.2025 · 26 citations
12. 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
13. Can Large Reasoning Models Self-Train?arXiv:2505.21444Sheikh Shafayat, Fahim Tajwar, Ruslan Salakhutdinov, et al.2025 · 16 citations
14. MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction FusionAnnual Meeting of the Association for Computational Linguistics (ACL)Qizhi Pei, Lijun Wu, Zhuoshi Pan, et al.2025 · 10 citations
15. Selective Preference Optimization via Token-Level Reward Function EstimationConference on Empirical Methods in Natural Language Processing (EMNLP)Kailai Yang, Zhiwei Liu, Qianqian Xie, et al.2025 · 8 citations
16. SCALER: Synthetic Scalable Adaptive Learning Environment for ReasoningFindings of the Association for Computational LinguisticsCaijun Xu, Changyi Xiao, Zhongyuan Peng, et al.2026 · 2 citations
17. What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test TimeAnnual Meeting of the Association for Computational Linguistics (ACL)Dong Yan, Jian Liang, Yanbo Wang, et al.2026 · 2 citations
18. Critical-CoT: A Robust Defense Framework against Reasoning-Level Backdoor Attacks in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Vu Tuan Truong, Long Bao Le2026
19. Exploiting Tree Structure for Credit Assignment in Reinforcement Learning with Large Language ModelsFindings of the Association for Computational LinguisticsHieu Tran, Zonghai Yao, Hong yu2026
20. Minimal Free Resolution Guided Adaptive Tree ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Dezhao Tang, Meihan Liu, Yulai Tong, et al.2026

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