---
title: "Who cites S²R: Teaching LLMs to Self-verify — 22 citing papers"
description: "22 papers of the Elamur Research library cite «S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning», found in their…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [S²R: Teaching LLMs to Self-verify and…](https://md.research.elamur.ai/free/en/papers/ma-2025-s-2-r-teaching-llms/) / cited by

# Who cites «S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning»

22 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 «S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning»](https://md.research.elamur.ai/free/en/papers/ma-2025-s-2-r-teaching-llms/)

1. **[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*
2. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
3. [**100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models arXiv:2505.00551**Chong Zhang, Yue Deng, Xiang Lin, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-100-days-after-deepseek/)
4. **[CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution](https://md.research.elamur.ai/free/en/papers/pan-2026-coverrl-breaking-consensus-trap-label/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Teng Pan, Yuchen Yan, Zixuan Wang, et al.*2026 · 1 citation*
5. **[A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions](https://md.research.elamur.ai/free/en/papers/yu-2026-survey-reinforcement-learning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.*2026*
6. **[Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection](https://md.research.elamur.ai/free/en/papers/zhang-2026-cultivating-forensic-reasoning-generalizable-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuchen Zhang, Yaxiong Wang, Kecheng Han, et al.*2026*
7. **[MDocRAG-RL: Empowering Multi-Modal Document RAG via Complex Visual Reasoning with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-mdocrag-rl-empowering-multi-modal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhongyu Wang*2026*
8. **[Policy-Guided Stepwise Action Planning for Controllable LLM Reasoning](https://md.research.elamur.ai/free/en/papers/zhou-2026-policy-guided-stepwise-action-planning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jianpeng Zhou, Qisheng Hu, Jiahai Wang, et al.*2026*
9. **[Self-Reflective Generation at Test Time](https://md.research.elamur.ai/free/en/papers/mu-2026-self-reflective-generation-test-time/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jian Mu, Qixin Zhang, Zhiyong Wang, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/ma-2025-s-2-r-teaching-llms/) — S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning