Elamur Research⌕
☰
home / papers / VAPO: Efficient and Reliable Reinforcement… / cited by

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»

  1. REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage NormalizationarXiv:2501.03262Jian Hu, Jason Klein Liu, Haotian Xu, et al.2025 · 104 citations
  2. Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language ModelsarXiv:2601.18734Siyan Zhao, Zhihui Xie, Mengchen Liu, et al.2026 · 97 citations
  3. ToolRL: Reward is All Tool Learning NeedsarXiv:2504.13958Cheng Qian, Emre Can Acikgoz, Qi He, et al.2025 · 94 citations
  4. Seed1.5-VL Technical ReportarXiv:2505.07062Dong Guo, Faming Wu, Feida Zhu, et al.2025 · 67 citations
  5. Reasoning with Exploration: An Entropy PerspectivearXiv:2506.14758Daixuan Cheng, Shaohan Huang, Xuekai Zhu, et al.2025 · 63 citations
  6. Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement LearningarXiv:2504.13914ByteDance Seed, Jiaze Chen, Tiantian Fan, et al.2025 · 62 citations
  7. UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement LearningarXiv:2509.02544Haoming Wang, Haoyang Zou, Huatong Song, et al.2025 · 26 citations
  8. Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from ExperiencearXiv:2512.17260Jiangjie Chen, Wenxiang Chen, Jiacheng Du, et al.2025 · 19 citations
  9. 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language ModelsarXiv:2505.00551Chong Zhang, Yue Deng, Xiang Lin, et al.2025 · 9 citations
  10. PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative VerifierarXiv:2506.10406Yuhua Jiang, Yuwen Xiong, Yufeng Yuan, et al.2025 · 8 citations

38 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/yue-2025-vapo-efficient-reliable-reinforcement-learning/cited-by/