---
title: "Who cites Feedback Loops With Language — 24 citing papers"
description: "24 papers of the Elamur Research library cite «Feedback Loops With Language Models Drive In-Context Reward Hacking», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/pan-2024-feedback-loops-language-models-drive/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/pan-2024-feedback-loops-language-models-drive/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Feedback Loops With Language Models Drive…](https://md.research.elamur.ai/free/en/papers/pan-2024-feedback-loops-language-models-drive/) / cited by

# Who cites «Feedback Loops With Language Models Drive In-Context Reward Hacking»

24 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «Feedback Loops With Language Models Drive In-Context Reward Hacking»](https://md.research.elamur.ai/free/en/papers/pan-2024-feedback-loops-language-models-drive/)

1. [**The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning arXiv:2403.03218**Nathaniel Li, Alexander Pan, Anjali Gopal, et al.*2024 · 127 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-wmdp-benchmark-measuring-reducing-malicious/)
2. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
3. **[Thinking beyond the anthropomorphic paradigm benefits LLM research](https://md.research.elamur.ai/free/en/papers/ibrahim-2026-thinking-beyond-anthropomorphic-paradigm-benefits/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lujain Ibrahim, Myra Cheng*2026 · 8 citations*
4. [**Workflow Closure Is Not Scientific Closure in Auto-Research Systems arXiv:2605.26200**Shuai Wang, Xinyuan Tian, Pangpang Liu, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/wang-2026-workflow-closure-scientific-closure/)
5. **[Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines](https://md.research.elamur.ai/free/en/papers/song-2026-alignment-tuning-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hwanjun Song*2026*
6. **[Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards](https://md.research.elamur.ai/free/en/papers/yuan-2026-curing-miracle-steps-llm-mathematical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Youliang Yuan, Qiuyang Mang, Jingbang Chen, et al.*2026*
7. **[LCO: LLM-based Constraint Optimization for Safer Agentic LLMs in Real-world Tasks](https://md.research.elamur.ai/free/en/papers/wan-2026-lco-llm-based-constraint-optimization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiayong Wan, Jiawei Chen, Zhaoxia Yin, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/pan-2024-feedback-loops-language-models-drive/) — Feedback Loops With Language Models Drive In-Context Reward Hacking