--- title: "Who cites VinePPO: Refining Credit — 62 citing papers" description: "62 papers of the Elamur Research library cite «VinePPO: Refining Credit Assignment in RL Training of LLMs», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/kazemnejad-2024-vineppo-refining-credit-assignment-rl/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kazemnejad-2024-vineppo-refining-credit-assignment-rl/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [VinePPO: Refining Credit Assignment in RL…](https://md.research.elamur.ai/free/en/papers/kazemnejad-2024-vineppo-refining-credit-assignment-rl/) / cited by # Who cites «VinePPO: Refining Credit Assignment in RL Training of LLMs» 62 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «VinePPO: Refining Credit Assignment in RL Training of LLMs»](https://md.research.elamur.ai/free/en/papers/kazemnejad-2024-vineppo-refining-credit-assignment-rl/) 1. [**DAPO: An Open-Source LLM Reinforcement Learning System at Scale arXiv:2503.14476**Qiying Yu, Zheng Zhang, Ruofei Zhu, et al.*2025 · 668 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-dapo-open-source-llm-reinforcement/) 2. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training arXiv:2411.15124**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 423 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/) 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. [**Process Reinforcement through Implicit Rewards arXiv:2502.01456**Ganqu Cui, Lifan Yuan, Zefan Wang, et al.*2025 · 151 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-process-reinforcement-through-implicit-rewards/) 5. [**Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs arXiv:2503.01307**Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.*2025 · 123 citations*](https://md.research.elamur.ai/free/en/papers/gandhi-2025-cognitive-behaviors-that-enable-self/) 6. [**Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models arXiv:2501.09686**Fengli Xu, Qianyue Hao, Zefang Zong, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-towards-large-reasoning-models-survey/) 7. [**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/) 8. **[Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://md.research.elamur.ai/free/en/papers/wang-2025-reinforcement-learning-reasoning-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.*2025 · 87 citations* 9. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/) 10. [**Reinforcement Learning via Self-Distillation arXiv:2601.20802**Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.*2026 · 74 citations*](https://md.research.elamur.ai/free/en/papers/hubotter-2026-reinforcement-learning-self-distillation/) 11. **[Group-in-Group Policy Optimization for LLM Agent Training](https://md.research.elamur.ai/free/en/papers/feng-2025-group-group-policy-optimization-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lang Feng, Zhenghai Xue, Tingcong Liu, et al.*2025 · 69 citations* 12. **[TreeRL: LLM Reinforcement Learning with On-Policy Tree Search](https://md.research.elamur.ai/free/en/papers/hou-2025-treerl-llm-reinforcement-learning-policy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhenyu Hou, Ziniu Hu, Yujiang Li, et al.*2025 · 26 citations* 13. **[SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks](https://md.research.elamur.ai/free/en/papers/wang-2026-sppo-sequence-level-ppo-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyi Wang, Yixia Li, Long Li, et al.*2026 · 8 citations* 14. **[AdapThink: Adaptive Thinking Preferences for Reasoning Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-adapthink-adaptive-thinking-preferences-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenyue Xu, Xu Wan, Wei Wang, et al.*2026* 15. **[Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization](https://md.research.elamur.ai/free/en/papers/wang-2026-enhancing-llm-based-search-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junzhe Wang, Zhiheng Xi, Yajie Yang, et al.*2026* 16. **[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* 17. **[Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement](https://md.research.elamur.ai/free/en/papers/sun-2026-learning-cognition-enhancing-rl-efficiency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zexu Sun, Yongcheng Zeng, Erxue Min, et al.*2026* 45 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kazemnejad-2024-vineppo-refining-credit-assignment-rl/) — VinePPO: Refining Credit Assignment in RL Training of LLMs