--- title: "Who cites Learning by Distilling Context — 60 citing papers" description: "60 papers of the Elamur Research library cite «Learning by Distilling Context», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/snell-2022-learning-distilling-context/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/snell-2022-learning-distilling-context/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Learning by Distilling Context](https://md.research.elamur.ai/free/en/papers/snell-2022-learning-distilling-context/) / cited by # Who cites «Learning by Distilling Context» 60 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · [← back to «Learning by Distilling Context»](https://md.research.elamur.ai/free/en/papers/snell-2022-learning-distilling-context/) 1. **[Large Language Models Can Self-Improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaxin Huang, Shixiang Gu, Le Hou, et al.*2023 · 575 citations* 2. [**Discovering Language Model Behaviors with Model-Written Evaluations arXiv:2212.09251**Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.*2022 · 328 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/) 3. **[Distilling Reasoning Capabilities into Smaller Language Models](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 219 citations* 4. **[Adapting Language Models to Compress Contexts](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexis Chevalier, Alexander Wettig, Anirudh Ajith, et al.*2023 · 182 citations* 5. **[Learning to Compress Prompts with Gist Tokens](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jesse Mu, Xiang Lisa Li, Noah Goodman*2023 · 178 citations* 6. [**Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training arXiv:2401.05566**Evan Hubinger, Carson Denison, Jesse Mu, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/hubinger-2024-sleeper-agents-training-deceptive-llms/) 7. [**Deliberative Alignment: Reasoning Enables Safer Language Models arXiv:2412.16339**Melody Y. Guan, Manas Joglekar, Eric Wallace, et al.*2024 · 115 citations*](https://md.research.elamur.ai/free/en/papers/guan-2024-deliberative-alignment-reasoning-enables-safer/) 8. [**RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation arXiv:2310.04408**Fangyuan Xu, Weijia Shi, Eunsol Choi*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-recomp-improving-retrieval-augmented-lms/) 9. [**In-context Autoencoder for Context Compression in a Large Language Model arXiv:2307.06945**Tao Ge, Jing Hu, Lei Wang, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/ge-2023-context-autoencoder-context-compression-large/) 10. [**Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models arXiv:2601.18734**Siyan Zhao, Zhihui Xie, Mengchen Liu, et al.*2026 · 97 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2026-self-distilled-reasoner-policy-self/) 11. [**The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions arXiv:2404.13208**Eric Wallace, Kai Xiao, Reimar Leike, et al.*2024 · 83 citations*](https://md.research.elamur.ai/free/en/papers/wallace-2024-instruction-hierarchy-training-llms-prioritize/) 12. [**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/) 13. [**SALMON: Self-Alignment with Instructable Reward Models arXiv:2310.05910**Zhiqing Sun, Yikang Shen, Hongxin Zhang, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-salmon-self-alignment-instructable-reward/) 14. **[Distilling an End-to-End Voice Assistant Without Instruction Training Data](https://md.research.elamur.ai/free/en/papers/held-2025-distilling-end-end-voice-assistant/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**William Held, Yanzhe Zhang, Minzhi Li, et al.*2025 · 18 citations* 15. **[Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/nath-2024-okay-let-s-do-this/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Abhijnan Nath, Shadi Manafi Avari, Avyakta Chelle, et al.*2024 · 16 citations* 16. **[LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?](https://md.research.elamur.ai/free/en/papers/wang-2026-lightreasoner-can-small-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingyuan Wang, Yankai Chen, Zhonghang Li, et al.*2026 · 1 citation* 44 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/snell-2022-learning-distilling-context/) — Learning by Distilling Context