---
title: "Who cites VAPO: Efficient and Reliable — 67 citing papers"
description: "67 papers of the Elamur Research library cite «VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks», found…"
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---
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# Who cites «VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks»

67 papers of our library cite it (of the 149,439 whose bibliographies we read) · 29 with a page here, the most cited first · [← back to «VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks»](https://md.research.elamur.ai/free/en/papers/yue-2025-vapo-efficient-reliable-reinforcement-learning/)

1. **[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*
2. **[Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-80-20-rule-high-entropy/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shenzhi Wang, Le Yu, Chang Gao, et al.*2025 · 152 citations*
3. **[Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model](https://md.research.elamur.ai/free/en/papers/hu-2025-open-reasoner-zero-open-source/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jingcheng Hu, Yinmin Zhang, Qi Han, et al.*2025 · 128 citations*
4. [**REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization arXiv:2501.03262**Jian Hu, Jason Klein Liu, Haotian Xu, et al.*2025 · 104 citations*](https://md.research.elamur.ai/free/en/papers/hu-2025-reinforce-stabilizing-critic-free-policy/)
5. [**Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models arXiv:2601.18734**Siyan Zhao, Zhihui Xie, Mengchen Liu, et al.*2026 · 97 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2026-self-distilled-reasoner-policy-self/)
6. [**ToolRL: Reward is All Tool Learning Needs arXiv:2504.13958**Cheng Qian, Emre Can Acikgoz, Qi He, et al.*2025 · 94 citations*](https://md.research.elamur.ai/free/en/papers/qian-2025-toolrl-reward-all-tool-learning/)
7. **[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*
8. **[AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning](https://md.research.elamur.ai/free/en/papers/fu-2025-areal-large-scale-asynchronous-reinforcement/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Wei Fu, Jiaxuan Gao, Xujie Shen, et al.*2025 · 80 citations*
9. [**Seed1.5-VL Technical Report arXiv:2505.07062**Dong Guo, Faming Wu, Feida Zhu, et al.*2025 · 67 citations*](https://md.research.elamur.ai/free/en/papers/guo-2025-seed1-5-vl-technical/)
10. [**Reasoning with Exploration: An Entropy Perspective arXiv:2506.14758**Daixuan Cheng, Shaohan Huang, Xuekai Zhu, et al.*2025 · 63 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2025-reasoning-exploration-entropy-perspective/)
11. [**Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning arXiv:2504.13914**ByteDance Seed, Jiaze Chen, Tiantian Fan, et al.*2025 · 62 citations*](https://md.research.elamur.ai/free/en/papers/bytedance-seed-2025-seed1-5-thinking-advancing/)
12. **[ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2025-prorl-prolonged-reinforcement-learning-expands/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Mingjie Liu, Shizhe Diao, Ximing Lu, et al.*2025 · 58 citations*
13. **[Absolute Zero: Reinforced Self-play Reasoning with Zero Data](https://md.research.elamur.ai/free/en/papers/zhao-2025-absolute-zero-reinforced-self-play/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Andrew Zhao, Yiran Wu, Yang Yue, et al.*2025 · 57 citations*
14. [**UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning arXiv:2509.02544**Haoming Wang, Haoyang Zou, Huatong Song, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-ui-tars-2-technical/)
15. [**Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience arXiv:2512.17260**Jiangjie Chen, Wenxiang Chen, Jiacheng Du, et al.*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-seed-prover-1-5/)
16. **[Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains](https://md.research.elamur.ai/free/en/papers/tan-2025-think-silently-think-fast-dynamic/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Wenhui Tan, Jiaze Li, Jianzhong Ju, et al.*2025 · 19 citations*
17. **[CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization](https://md.research.elamur.ai/free/en/papers/peng-2026-criticlean-critic-guided-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhongyuan Peng, Yifan Yao, Kaijing Ma, et al.*2026 · 12 citations*
18. **[Reinforced Efficient Reasoning via Semantically Diverse Exploration](https://md.research.elamur.ai/free/en/papers/zhao-2026-reinforced-efficient-reasoning-semantically-diverse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziqi Zhao, Zhaochun Ren, Jiahong Zou, et al.*2026 · 9 citations*
19. [**100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models arXiv:2505.00551**Chong Zhang, Yue Deng, Xiang Lin, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-100-days-after-deepseek/)
20. [**PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier arXiv:2506.10406**Yuhua Jiang, Yuwen Xiong, Yufeng Yuan, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2025-pag-multi-turn-reinforced-llm/)
21. **[NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2025-nover-incentive-training-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Liu, Siya Qi, Xinyu Wang, et al.*2025 · 7 citations*
22. **[RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization](https://md.research.elamur.ai/free/en/papers/dong-2026-rl-plus-countering-capability-boundary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihong Dong, Xue Jiang, Yongding Tao, et al.*2026 · 6 citations*
23. **[Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards](https://md.research.elamur.ai/free/en/papers/tang-2026-rethinking-sample-polarity-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Tang, Yuliang Zhan, Zhixun Li, et al.*2026 · 4 citations*
24. **[Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/lei-2026-writing-rl-advancing-long-form/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuanyu Lei, Chenliang Li, Yuning Wu, et al.*2026 · 4 citations*
25. **[SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis](https://md.research.elamur.ai/free/en/papers/wu-2026-synthrl-scaling-visual-reasoning-verifiable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zijian Wu, Jinjie Ni, Xiangyan Liu, et al.*2026 · 3 citations*
26. **[MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization](https://md.research.elamur.ai/free/en/papers/zhao-2026-maestro-meta-learning-adaptive-estimation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Zhao, Hepeng Wang, Xiao Ding, et al.*2026 · 1 citation*
27. **[Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment](https://md.research.elamur.ai/free/en/papers/zhang-2026-generalizable-llm-learning-graph-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizhuo Zhang, Heng Wang, Shangbin Feng, et al.*2026*
28. **[Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/mei-2026-good-reasoning-makes-good-demonstrations/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tiehua Mei, Minxuan Lv, Leiyu Pan, et al.*2026*
29. **[Tailored Primitive Initialization is the Secret Key to Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yao-2026-tailored-primitive-initialization-is-secret/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihang Yao, Guangtao Zeng, Raina Wu, et al.*2026*

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

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