---
title: "Who cites StepHint: Multi-level Stepwise — 13 citing papers"
description: "13 papers of the Elamur Research library cite «StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason», found in their…"
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---
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# 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»](https://md.research.elamur.ai/free/en/papers/zhang-2026-stephint-multi-level-stepwise-hints/)

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 paper**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 216 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/)
2. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang 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 Error](https://md.research.elamur.ai/free/en/papers/tang-2026-do-not-step-into-same/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Learning](https://md.research.elamur.ai/free/en/papers/li-2026-no-more-stale-feedback-co/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Perspective](https://md.research.elamur.ai/free/en/papers/qian-2026-improved-policy-optimization-mixture-experts/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yining Qian, Jinpeng Li, Fei Mi, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/zhang-2026-stephint-multi-level-stepwise-hints/) — StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason