🔗 Who cites «Can Large Reasoning Models Self-Train?»

16 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · ← back to «Can Large Reasoning Models Self-Train?»

1. Spurious Rewards: Rethinking Training Signals in RLVRarXiv:2506.10947Rulin Shao, Shuyue Stella Li, Rui Xin, et al.2025 · 53 citations
2. Learning to Reason without External RewardsarXiv:2505.19590Xuandong Zhao, Zhewei Kang, Aosong Feng, et al.2025 · 44 citations
3. A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super IntelligencearXiv:2507.21046Huan-ang Gao, Jiayi Geng, Wenyue Hua, et al.2025 · 36 citations
4. Outcome-based Exploration for LLM ReasoningarXiv:2509.06941Yuda Song, Julia Kempe, Remi Munos2025 · 15 citations
5. What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test TimeAnnual Meeting of the Association for Computational Linguistics (ACL)Dong Yan, Jian Liang, Yanbo Wang, et al.2026 · 2 citations
6. CoAct: Co-Active LLM Preference Learning with Human-AI SynergyAnnual Meeting of the Association for Computational Linguistics (ACL)Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.2026 · 1 citation
7. Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement LearningFindings of the Association for Computational LinguisticsZhiyin Yu, Bo Zhang, Qibin Hou, et al.2026 · 1 citation
8. A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and SolutionsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.2026
9. Verifier-Free RL for LLMs via Intrinsic Gradient-Norm RewardFindings of the Association for Computational LinguisticsXuexiang Wen, Hang Yu, Linchao Zhu, et al.2026

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

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