--- title: "Who cites Advantage-Weighted Regression: — 65 citing papers" description: "65 papers of the Elamur Research library cite «Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning»…" canonical: "https://research.elamur.ai/free/en/papers/peng-2019-advantage-weighted-regression-simple-scalable/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/peng-2019-advantage-weighted-regression-simple-scalable/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Advantage-Weighted Regression: Simple and…](https://md.research.elamur.ai/free/en/papers/peng-2019-advantage-weighted-regression-simple-scalable/) / cited by # Who cites «Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning» 65 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning»](https://md.research.elamur.ai/free/en/papers/peng-2019-advantage-weighted-regression-simple-scalable/) 1. [**Direct Preference Optimization: Your Language Model is Secretly a Reward Model arXiv:2305.18290**Rafael Rafailov, Archit Sharma, Eric Mitchell, et al.*2023 · 2,896 citations*](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/) 2. [**OpenChat: Advancing Open-source Language Models with Mixed-Quality Data arXiv:2309.11235**Guan Wang, Sijie Cheng, Xianyuan Zhan, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-openchat-advancing-open-source-language/) 3. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/) 4. **[Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://md.research.elamur.ai/free/en/papers/qu-2024-recursive-introspection-teaching-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.*2024 · 75 citations* 5. [**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/) 6. **[Group-in-Group Policy Optimization for LLM Agent Training](https://md.research.elamur.ai/free/en/papers/feng-2025-group-group-policy-optimization-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lang Feng, Zhenghai Xue, Tingcong Liu, et al.*2025 · 69 citations* 7. **[Trial and Error: Exploration-Based Trajectory Optimization of LLM Agents](https://md.research.elamur.ai/free/en/papers/song-2024-trial-error-exploration-based-trajectory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Song, Da Yin, Xiang Yue, et al.*2024 · 53 citations* 8. **[WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wei-2025-webagent-r1-training-web-agents/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhepei Wei, Wenlin Yao, Yao Liu, et al.*2025 · 36 citations* 9. [**An Emulator for Fine-Tuning Large Language Models using Small Language Models arXiv:2310.12962**Eric Mitchell, Rafael Rafailov, Archit Sharma, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-emulator-fine-tuning-large-language/) 10. **[Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2024-weak-strong-search-align-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhanhui Zhou, Zhixuan Liu, Jie Liu, et al.*2024 · 17 citations* 11. **[Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions](https://md.research.elamur.ai/free/en/papers/gan-2026-android-coach-improve-online-agentic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guo Gan, Yuxuan Ding, Cong Chen, et al.*2026 · 7 citations* 12. **[Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents](https://md.research.elamur.ai/free/en/papers/zhen-2026-hierarchical-reinforcement-learning-augmented-step/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuai Zhen, Yanhua Yu, Ruopei Guo, et al.*2026 · 1 citation* 53 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/peng-2019-advantage-weighted-regression-simple-scalable/) — Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning