🔗 Who cites «Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback»

12 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback»

1. OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF FrameworkarXiv:2405.11143Jian Hu, Xibin Wu, Wei Shen, et al.2024 · 140 citations
2. REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage NormalizationarXiv:2501.03262Jian Hu, Jason Klein Liu, Haotian Xu, et al.2025 · 104 citations
3. A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?arXiv:2503.24235Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.2025 · 79 citations
4. VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning TasksarXiv:2504.05118Yu Yue, Yufeng Yuan, Qiying Yu, et al.2025 · 68 citations
5. Seed1.5-VL Technical ReportarXiv:2505.07062Dong Guo, Faming Wu, Feida Zhu, et al.2025 · 67 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. A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to ReproducibilityarXiv:2504.07086Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao, et al.2025 · 36 citations
8. Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward ModelsFindings of the Association for Computational LinguisticsQiyuan Zhang, Yufei Wang, Tianhe Wu, et al.2026

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

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