---
title: "Who cites Exploring Data Scaling Trends — 12 citing papers"
description: "12 papers of the Elamur Research library cite «Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback»…"
canonical: "https://research.elamur.ai/free/en/papers/shen-2025-exploring-data-scaling-trends-effects/cited-by/"
---
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# 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»](https://md.research.elamur.ai/free/en/papers/shen-2025-exploring-data-scaling-trends-effects/)

1. [**OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework arXiv:2405.11143**Jian Hu, Xibin Wu, Wei Shen, et al.*2024 · 140 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-openrlhf-easy-use-scalable/)
2. [**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/)
3. [**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/)
4. [**VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks arXiv:2504.05118**Yu Yue, Yufeng Yuan, Qiying Yu, et al.*2025 · 68 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-vapo-efficient-reliable-reinforcement-learning/)
5. [**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/)
6. [**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/)
7. [**A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility arXiv:2504.07086**Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao, et al.*2025 · 36 citations*](https://md.research.elamur.ai/free/en/papers/hochlehnert-2025-sober-look-progress-language-model/)
8. **[Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-length-scaling-synergizing-breadth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiyuan Zhang, Yufei Wang, Tianhe Wu, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/shen-2025-exploring-data-scaling-trends-effects/) — Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback