---
title: "Who cites Iterative Preference Learning from — 92 citing papers"
description: "92 papers of the Elamur Research library cite «Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF…"
canonical: "https://research.elamur.ai/free/en/papers/xiong-2023-iterative-preference-learning-human-feedback/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/xiong-2023-iterative-preference-learning-human-feedback/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Iterative Preference Learning from Human…](https://md.research.elamur.ai/free/en/papers/xiong-2023-iterative-preference-learning-human-feedback/) / cited by

# Who cites «Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint»

92 papers of our library cite it (of the 149,439 whose bibliographies we read) · 27 with a page here, the most cited first · [← back to «Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint»](https://md.research.elamur.ai/free/en/papers/xiong-2023-iterative-preference-learning-human-feedback/)

1. **[Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/wang-2024-interpretable-preferences-multi-objective-reward/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoxiang Wang, Wei Xiong, Tengyang Xie, et al.*2024 · 153 citations*
2. **[Iterative Reasoning Preference Optimization](https://md.research.elamur.ai/free/en/papers/pang-2024-iterative-reasoning-preference-optimization/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.*2024 · 153 citations*
3. [**Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing arXiv:2406.08464**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 148 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/)
4. [**RLHF Workflow: From Reward Modeling to Online RLHF arXiv:2405.07863**Hanze Dong, Wei Xiong, Bo Pang, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-rlhf-workflow-reward-modeling-online/)
5. **[A Survey of Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Timo Kaufmann, Paul Weng, Viktor Bengs, et al.*2023 · 92 citations*
6. **[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://md.research.elamur.ai/free/en/papers/wang-2024-arithmetic-control-llms-diverse-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxiang Wang, Yong Lin, Wei Xiong, et al.*2024 · 68 citations*
7. **[OffsetBias: Leveraging Debiased Data for Tuning Evaluators](https://md.research.elamur.ai/free/en/papers/park-2024-offsetbias-leveraging-debiased-data-tuning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Junsoo Park, Seungyeon Jwa, Ren Meiying, et al.*2024 · 67 citations*
8. [**CycleResearcher: Improving Automated Research via Automated Review arXiv:2411.00816**Yixuan Weng, Minjun Zhu, Guangsheng Bao, et al.*2024 · 65 citations*](https://md.research.elamur.ai/free/en/papers/weng-2024-cycleresearcher-improving-automated-research/)
9. **[The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism](https://md.research.elamur.ai/free/en/papers/song-2025-good-bad-greedy-evaluation-llms/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yifan Song, Guoyin Wang, Sujian Li, et al.*2025 · 64 citations*
10. **[mDPO: Conditional Preference Optimization for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-mdpo-conditional-preference-optimization-multimodal/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fei Wang, Wenxuan Zhou, James Y. Huang, et al.*2024 · 50 citations*
11. **[LiPO: Listwise Preference Optimization through Learning-to-Rank](https://md.research.elamur.ai/free/en/papers/liu-2025-lipo-listwise-preference-optimization-through/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tianqi Liu, Zhen Qin, Junru Wu, et al.*2025 · 31 citations*
12. [**LLM-as-a-Judge & Reward Model: What They Can and Cannot Do arXiv:2409.11239**Guijin Son, Hyunwoo Ko, Hoyoung Lee, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/son-2024-llm-judge-reward-model-they/)
13. **[Aligning LLMs with Individual Preferences via Interaction](https://md.research.elamur.ai/free/en/papers/wu-2025-aligning-llms-individual-preferences-interaction/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Shujin Wu, Yi R. Fung, Cheng Qian, et al.*2025 · 24 citations*
14. [**Preference Optimization for Reasoning with Pseudo Feedback arXiv:2411.16345**Fangkai Jiao, Geyang Guo, Xingxing Zhang, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/jiao-2024-preference-optimization-reasoning-pseudo-feedback/)
15. **[MDCure: A Scalable Pipeline for Multi-Document Instruction-Following](https://md.research.elamur.ai/free/en/papers/liu-2025-mdcure-scalable-pipeline-multi-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, et al.*2025 · 11 citations*
16. [**Yi-Lightning Technical Report arXiv:2412.01253**Alan Wake, Bei Chen, C.X. Lv, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/wake-2024-yi-lightning-technical-report/)
17. **[WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback](https://md.research.elamur.ai/free/en/papers/shi-2026-wildfeedback-aligning-llms-situ-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taiwei Shi, Zhuoer Wang, Longqi Yang, et al.*2026 · 9 citations*
18. **[Selective Preference Optimization via Token-Level Reward Function Estimation](https://md.research.elamur.ai/free/en/papers/yang-2025-selective-preference-optimization-token-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kailai Yang, Zhiwei Liu, Qianqian Xie, et al.*2025 · 8 citations*
19. **[Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems](https://md.research.elamur.ai/free/en/papers/yang-2026-query-efficient-agentic-graph-extraction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuhua Yang, Jiahao Zhang, Yilong Wang, et al.*2026 · 4 citations*
20. **[Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment](https://md.research.elamur.ai/free/en/papers/tan-2026-beyond-compromise-pareto-lenient-consensus/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Renxuan Tan, Rongpeng Li, Zhifeng Zhao, et al.*2026 · 1 citation*
21. **[EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention](https://md.research.elamur.ai/free/en/papers/zhang-2026-eyemulator-improving-code-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Zhang, Chen Huang, Yueke Zhang, et al.*2026 · 1 citation*
22. **[From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation](https://md.research.elamur.ai/free/en/papers/wang-2026-off-policy-policy-enhancing-gui/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zezhou Wang, Ziyun Zhang, Xiaoyi Zhang, et al.*2026 · 1 citation*
23. **[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*
24. **[COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values](https://md.research.elamur.ai/free/en/papers/wu-2026-coig-p-high-quality-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Siwei Wu, JinCheng Ren, Xeron Du, et al.*2026*
25. **[Divergent Thinking: Escape the Homogeneity Trap in Generative Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/tao-2026-divergent-thinking-escape-homogeneity-trap/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiheng Tao, Kaiwen Cheng, Zhiwei Nie, et al.*2026*
26. **[Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning](https://md.research.elamur.ai/free/en/papers/zhou-2026-enhancing-reinforcement-learning-radiology-report/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qin Zhou, Guoyan Liang, Qianyi Yang, et al.*2026*
27. **[Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling](https://md.research.elamur.ai/free/en/papers/pathmanathan-2026-teach-reward-model-correct-itself/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pankayaraj Pathmanathan, Furong Huang*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/xiong-2023-iterative-preference-learning-human-feedback/) — Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint