--- title: "Who cites Kimi k1.5: Scaling Reinforcement Learning with…" description: "401 papers of the Elamur Research library cite «Kimi k1.5: Scaling Reinforcement Learning with LLMs», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Kimi k1.5: Scaling Reinforcement Learning with…](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/) / cited by # Who cites «Kimi k1.5: Scaling Reinforcement Learning with LLMs» 401 papers of our library cite it (of the 140,884 whose bibliographies we read) · 18 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/) 1. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) 2. [**DAPO: An Open-Source LLM Reinforcement Learning System at Scale**Qiying Yu, Zheng Zhang, Ruofei Zhu, et al.*2025 · 634 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-dapo-open-source-llm-reinforcement/) 3. [**s1: Simple test-time scaling**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 584 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) 4. [**Kimi K2: Open Agentic Intelligence**Kimi Team, Yifan Bai, Yiping Bao, et al.*2025 · 331 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k2-open-agentic/) 5. [**Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 196 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/) 6. [**Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs**Xingyu Chen, Jiahao Xu, Tian Liang, et al.*2024 · 188 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-think-that-much-2-3/) 7. [**Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 174 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) 8. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 153 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/) 9. [**SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild**Weihao Zeng, Yuzhen Huang, Qian Liu, et al.*2025 · 148 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2025-simplerl-zoo-investigating-taming-zero/) 10. [**Process Reinforcement through Implicit Rewards**Ganqu Cui, Lifan Yuan, Zefan Wang, et al.*2025 · 147 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-process-reinforcement-through-implicit-rewards/) 11. [**Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning**Shenzhi Wang, Le Yu, Chang Gao, et al.*2025 · 142 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-80-20-rule-high-entropy/) 12. [**R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning**Huatong Song, Jinhao Jiang, Yingqian Min, et al.*2025 · 137 citations*](https://md.research.elamur.ai/free/en/papers/song-2025-r1-searcher-incentivizing-search/) 13. [**Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model**Jingcheng Hu, Yinmin Zhang, Qi Han, et al.*2025 · 121 citations*](https://md.research.elamur.ai/free/en/papers/hu-2025-open-reasoner-zero-open-source/) 14. [**Demystifying Long Chain-of-Thought Reasoning in LLMs**Edward Yeo, Yuxuan Tong, Morry Niu, et al.*2025 · 113 citations*](https://md.research.elamur.ai/free/en/papers/yeo-2025-demystifying-long-chain-thought-reasoning/) 15. [**DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments**Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, et al.*2025 · 108 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-deepresearcher-scaling-deep-research-reinforcement/) 16. [**Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning**Tian Xie, Zitian Gao, Qingnan Ren, et al.*2025 · 103 citations*](https://md.research.elamur.ai/free/en/papers/xie-2025-logic-rl-unleashing-llm/) 17. [**Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling**Runze Liu, Junqi Gao, Jian Zhao, et al.*2025 · 41 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-1b-llm-surpass-405b-llm/) 18. [**Multi-Agent Debate for Machine Translation: A Case Study on English-Japanese Translation**Zhan Shen, Jason Naradowsky, Xiaotian Wang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/shen-2026-multi-agent-debate-machine-translation/) 383 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/) — Kimi k1.5: Scaling Reinforcement Learning with LLMs