---
title: "Who cites The Surprising Effectiveness of — 32 citing papers"
description: "32 papers of the Elamur Research library cite «The Surprising Effectiveness of Test-Time Training for Few-Shot Learning», found in their…"
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# Who cites «The Surprising Effectiveness of Test-Time Training for Few-Shot Learning»

32 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «The Surprising Effectiveness of Test-Time Training for Few-Shot Learning»](https://md.research.elamur.ai/free/en/papers/akyurek-2024-surprising-effectiveness-test-time-training/)

1. **[DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**DeepSeek-AI, Daya Guo, Dejian Yang, et al.*2025 · 4,728 citations*
2. **[TTRL: Test-Time Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zuo-2025-ttrl-test-time-reinforcement-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxin Zuo, Kaiyan Zhang, Li Sheng, et al.*2025 · 76 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. **[Aristotle: IMO-level Automated Theorem Proving](https://md.research.elamur.ai/free/en/papers/achim-2025-aristotle-imo-level-automated-theorem/) [SuperIntelligence - Robotics - Safety & Alignment](https://md.research.elamur.ai/free/en/venues/superintelligence-robotics-safety-alignment/)**Tudor Achim, Alex Best, Alberto Bietti, et al.*2025 · 29 citations*
5. [**Training a Scientific Reasoning Model for Chemistry arXiv:2506.17238**Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2025-training-scientific-reasoning-model/)
6. **[Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering](https://md.research.elamur.ai/free/en/papers/jurayj-2025-is-that-your-final-answer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**William Jurayj, Jeffrey Cheng, Benjamin Van Durme*2025 · 11 citations*
7. [**Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement arXiv:2505.07961**Xuechen Zhang, Zijian Huang, Chenshun Ni, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-making-small-language-models-efficient/)
8. **[Gated Differentiable Working Memory for Long-Context Language Modeling](https://md.research.elamur.ai/free/en/papers/mei-2026-gated-differentiable-working-memory-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingrui Mei, Shenghua Liu, Yiwei Wang, et al.*2026 · 4 citations*
9. **[Test-Time Training for Zero-Resource Dense Retrieval Reranking](https://md.research.elamur.ai/free/en/papers/liu-2026-test-time-training-zero-resource/) [Workshop on Towards Knowledgeable Foundation Models](https://md.research.elamur.ai/free/en/venues/knowfm/)**Shiyan Liu, Yichen Li*2026*
10. **[Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks](https://md.research.elamur.ai/free/en/papers/wang-2026-your-reasoning-benchmark-may-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinhe Wang, Jin Huang, Xingjian Zhang, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/akyurek-2024-surprising-effectiveness-test-time-training/) — The Surprising Effectiveness of Test-Time Training for Few-Shot Learning