---
title: "Who cites Following Length Constraints in Instructions"
description: "19 papers of the Elamur Research library cite «Following Length Constraints in Instructions», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/yuan-2025-following-length-constraints-instructions/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yuan-2025-following-length-constraints-instructions/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Following Length Constraints in Instructions](https://md.research.elamur.ai/free/en/papers/yuan-2025-following-length-constraints-instructions/) / cited by

# Who cites «Following Length Constraints in Instructions»

19 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «Following Length Constraints in Instructions»](https://md.research.elamur.ai/free/en/papers/yuan-2025-following-length-constraints-instructions/)

1. [**L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning arXiv:2503.04697**Pranjal Aggarwal, Sean Welleck*2025 · 125 citations*](https://md.research.elamur.ai/free/en/papers/aggarwal-2025-l1-controlling-long-reasoning-model/)
2. [**LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs arXiv:2408.07055**Yushi Bai, Jiajie Zhang, Xin Lv, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longwriter-unleashing-10-000/)
3. **[Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge](https://md.research.elamur.ai/free/en/papers/wu-2025-meta-rewarding-language-models-self/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianhao Wu, Weizhe Yuan, Olga Golovneva, et al.*2025 · 64 citations*
4. [**Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models arXiv:2503.24377**Rui Wang, Hongru Wang, Boyang Xue, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-harnessing-reasoning-economy-survey-efficient/)
5. **[Exploring Iterative Controllable Summarization with Large Language Models](https://md.research.elamur.ai/free/en/papers/ryu-2026-exploring-iterative-controllable-summarization-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sangwon Ryu, Heejin Do, Daehui Kim, et al.*2026 · 2 citations*
6. **[CLARO: Controlled Attribute-Driven Reasoning Optimization for Efficient Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/schlesinger-2026-claro-controlled-attribute-driven-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Oded Schlesinger, Young Kyung Kim, J. Matias Di Martino, 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/yuan-2025-following-length-constraints-instructions/) — Following Length Constraints in Instructions
