🔗 Who cites «StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason»

13 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · ← back to «StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason»

1. Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?arXiv:2504.13837 · NeurIPS 2025 Oral; ICML 2025 AI4MATH workshop best paperYang Yue, Zhiqi Chen, Rui Lu, et al.2025 · 216 citations
2. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
3. Do Not Step Into the Same River Twice: Learning to Reason from Trial and ErrorAnnual Meeting of the Association for Computational Linguistics (ACL)Chenming Tang, Hsiu-Yuan Huang, Weijie Liu, et al.2026 · 3 citations
4. No More Stale Feedback: Co-Evolving Critics for Open-World Agent LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Zhicong Li, Lingjie Jiang, Yulan Hu, et al.2026 · 3 citations
5. Improved Policy Optimization for Mixture-of-Experts Models: Importance Sampling and Rewarding from an Expert-Centric PerspectiveFindings of the Association for Computational LinguisticsYining Qian, Jinpeng Li, Fei Mi, et al.2026

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

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