---
title: "Who cites TTRL: Test-Time Reinforcement — 70 citing papers"
description: "70 papers of the Elamur Research library cite «TTRL: Test-Time Reinforcement Learning», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/zuo-2025-ttrl-test-time-reinforcement-learning/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [TTRL: Test-Time Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zuo-2025-ttrl-test-time-reinforcement-learning/) / cited by

# Who cites «TTRL: Test-Time Reinforcement Learning»

70 papers of our library cite it (of the 149,439 whose bibliographies we read) · 26 with a page here, the most cited first · [← back to «TTRL: Test-Time Reinforcement Learning»](https://md.research.elamur.ai/free/en/papers/zuo-2025-ttrl-test-time-reinforcement-learning/)

1. [**The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models arXiv:2505.22617**Ganqu Cui, Yuchen Zhang, Jiacheng Chen, et al.*2025 · 101 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-entropy-mechanism-reinforcement-learning-reasoning/)
2. **[Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://md.research.elamur.ai/free/en/papers/wang-2025-reinforcement-learning-reasoning-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.*2025 · 87 citations*
3. [**Reinforcement Learning via Self-Distillation arXiv:2601.20802**Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.*2026 · 74 citations*](https://md.research.elamur.ai/free/en/papers/hubotter-2026-reinforcement-learning-self-distillation/)
4. **[Learning to Reason under Off-Policy Guidance](https://md.research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jianhao Yan, Yafu Li, Zican Hu, et al.*2025 · 68 citations*
5. [**Reasoning with Exploration: An Entropy Perspective arXiv:2506.14758**Daixuan Cheng, Shaohan Huang, Xuekai Zhu, et al.*2025 · 63 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2025-reasoning-exploration-entropy-perspective/)
6. **[Absolute Zero: Reinforced Self-play Reasoning with Zero Data](https://md.research.elamur.ai/free/en/papers/zhao-2025-absolute-zero-reinforced-self-play/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Andrew Zhao, Yiran Wu, Yang Yue, et al.*2025 · 57 citations*
7. [**Spurious Rewards: Rethinking Training Signals in RLVR arXiv:2506.10947**Rulin Shao, Shuyue Stella Li, Rui Xin, et al.*2025 · 53 citations*](https://md.research.elamur.ai/free/en/papers/shao-2025-spurious-rewards-rethinking-training-signals/)
8. **[WebDancer: Towards Autonomous Information Seeking Agency](https://md.research.elamur.ai/free/en/papers/wu-2025-webdancer-towards-autonomous-information-seeking/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jialong Wu, Baixuan Li, Runnan Fang, et al.*2025 · 50 citations*
9. **[AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time](https://md.research.elamur.ai/free/en/papers/zhang-2025-alphaone-reasoning-models-thinking-slow/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junyu Zhang, Runpei Dong, Han Wang, et al.*2025 · 10 citations*
10. **[ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning](https://md.research.elamur.ai/free/en/papers/wu-2026-atlas-orchestrating-heterogeneous-models-tools/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyang Wu, Guocheng Zhai, Ruihan Jin, et al.*2026 · 8 citations*
11. **[TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/wu-2026-templaterl-structured-template-guided-reinforcement/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyang Wu, Chonghua Liao, Mingkuan Feng, et al.*2026 · 8 citations*
12. **[Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-beyond-majority-voting-fine-grained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weiqin Wang, Yile Wang, Kehao Chen, et al.*2026 · 4 citations*
13. **[Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification](https://md.research.elamur.ai/free/en/papers/wan-2026-inference-time-scaling-verification-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxuan Wan, Tianqing Fang, Zaitang LI, et al.*2026 · 4 citations*
14. **[What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time](https://md.research.elamur.ai/free/en/papers/yan-2026-what-if-consensus-lies-selective/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dong Yan, Jian Liang, Yanbo Wang, et al.*2026 · 2 citations*
15. **[Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models](https://md.research.elamur.ai/free/en/papers/wu-2026-anti-length-shift-dynamic-outlier/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Wu, Liyi Chen, Congxi Xiao, et al.*2026 · 1 citation*
16. **[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*
17. **[Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yu-2026-easy-samples-are-all-you/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiyin Yu, Bo Zhang, Qibin Hou, et al.*2026 · 1 citation*
18. [**Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad?Level Mathematical Problem Solving arXiv:2512.10739**Yuzhe Gu, Songyang Gao, Zijian Wu, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/gu-2025-intern-s1-mo-long/)
19. **[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*
20. **[GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training](https://md.research.elamur.ai/free/en/papers/xie-2026-gala-geometric-data-selection-strategic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhongwei Xie, Ruihao Liao, Zimo Wang, et al.*2026*
21. **[Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment](https://md.research.elamur.ai/free/en/papers/zhang-2026-generalizable-llm-learning-graph-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizhuo Zhang, Heng Wang, Shangbin Feng, et al.*2026*
22. **[The Retrieval Bottleneck: Scaling Laws for Reinforcement Learning in RAG](https://md.research.elamur.ai/free/en/papers/zhou-2026-retrieval-bottleneck-scaling-laws-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shu Zhou, Jinman Leng, Yufei Song, et al.*2026*
23. **[Training-Free Test-Time Contrastive Learning for Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-training-free-test-time-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaiwen Zheng, Kai Zhou, Jinwu Hu, et al.*2026*
24. **[Triviality Corrected Endogenous Reward](https://md.research.elamur.ai/free/en/papers/wang-2026-triviality-corrected-endogenous-reward/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinda Wang, Zhengxu Hou, Yangshijie Zhang, et al.*2026*
25. **[Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2026-understanding-mitigating-spurious-signal-amplification/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yongcan Yu, Lingxiao He, Jian Liang, et al.*2026*
26. **[Verifier-Free RL for LLMs via Intrinsic Gradient-Norm Reward](https://md.research.elamur.ai/free/en/papers/wen-2026-verifier-free-rl-llms-intrinsic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuexiang Wen, Hang Yu, Linchao Zhu, et al.*2026*

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

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