--- title: "Who cites RL on Incorrect Synthetic Data — 50 citing papers" description: "50 papers of the Elamur Research library cite «RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold»…" canonical: "https://research.elamur.ai/free/en/papers/setlur-2024-rl-incorrect-synthetic-data-scales/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/setlur-2024-rl-incorrect-synthetic-data-scales/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [RL on Incorrect Synthetic Data Scales the…](https://md.research.elamur.ai/free/en/papers/setlur-2024-rl-incorrect-synthetic-data-scales/) / cited by # 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»](https://md.research.elamur.ai/free/en/papers/setlur-2024-rl-incorrect-synthetic-data-scales/) 1. [**Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters arXiv:2408.03314**Charlie Snell, Jaehoon Lee, Kelvin Xu, et al.*2024 · 567 citations*](https://md.research.elamur.ai/free/en/papers/snell-2024-scaling-llm-test-time-compute/) 2. **[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* 3. [**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/) 4. [**Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning arXiv:2410.08146**Amrith Setlur, Chirag Nagpal, Adam Fisch, et al.*2024 · 93 citations*](https://md.research.elamur.ai/free/en/papers/setlur-2024-rewarding-progress-scaling-automated-process/) 5. [**Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents arXiv:2408.07199**Pranav Putta, Edmund Mills, Naman Garg, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/putta-2024-agent-q-advanced-reasoning-learning/) 6. [**VinePPO: Refining Credit Assignment in RL Training of LLMs arXiv:2410.01679**Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, et al.*2024 · 63 citations*](https://md.research.elamur.ai/free/en/papers/kazemnejad-2024-vineppo-refining-credit-assignment-rl/) 7. [**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/) 8. **[Improve Vision Language Model Chain-of-thought Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2025-improve-vision-language-model-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruohong Zhang, Bowen Zhang, Yanghao Li, et al.*2025 · 42 citations* 9. [**Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling arXiv:2502.06703**Runze Liu, Junqi Gao, Jian Zhao, et al.*2025 · 41 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-1b-llm-surpass-405b-llm/) 10. [**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/) 11. **[S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/ma-2025-s-2-r-teaching-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 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/) 13. [**Can Large Reasoning Models Self-Train? arXiv:2505.21444**Sheikh Shafayat, Fahim Tajwar, Ruslan Salakhutdinov, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/shafayat-2025-large-reasoning-models-self-train/) 14. **[MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion](https://md.research.elamur.ai/free/en/papers/pei-2025-mathfusion-enhancing-mathematical-problem-solving/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qizhi Pei, Lijun Wu, Zhuoshi Pan, et al.*2025 · 10 citations* 15. **[Selective Preference Optimization via Token-Level Reward Function Estimation](https://md.research.elamur.ai/free/en/papers/yang-2025-selective-preference-optimization-token-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kailai Yang, Zhiwei Liu, Qianqian Xie, et al.*2025 · 8 citations* 16. **[SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-scaler-synthetic-scalable-adaptive-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Caijun Xu, Changyi Xiao, Zhongyuan Peng, et al.*2026 · 2 citations* 17. **[What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time](https://md.research.elamur.ai/free/en/papers/yan-2026-what-if-consensus-lies-selective/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Models](https://md.research.elamur.ai/free/en/papers/truong-2026-critical-cot-robust-defense-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vu Tuan Truong, Long Bao Le*2026* 19. **[Exploiting Tree Structure for Credit Assignment in Reinforcement Learning with Large Language Models](https://md.research.elamur.ai/free/en/papers/tran-2026-exploiting-tree-structure-credit-assignment/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hieu Tran, Zonghai Yao, Hong yu*2026* 20. **[Minimal Free Resolution Guided Adaptive Tree Reasoning](https://md.research.elamur.ai/free/en/papers/tang-2026-minimal-free-resolution-guided-adaptive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dezhao Tang, Meihan Liu, Yulai Tong, et al.*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/setlur-2024-rl-incorrect-synthetic-data-scales/) — RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold