--- title: "Who cites Unpacking DPO and PPO: — 43 citing papers" description: "43 papers of the Elamur Research library cite «Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference…" canonical: "https://research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Unpacking DPO and PPO: Disentangling Best…](https://md.research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/) / cited by # Who cites «Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback» 43 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback»](https://md.research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/) 1. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training arXiv:2411.15124**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 423 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/) 2. [**SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model arXiv:2502.02737**Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.*2025 · 181 citations*](https://md.research.elamur.ai/free/en/papers/allal-2025-smollm2-smol-goes-big-data/) 3. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations* 4. **[Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://md.research.elamur.ai/free/en/papers/liu-2025-culturally-aware-adapted-nlp-taxonomy/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen*2025 · 90 citations* 5. [**RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style arXiv:2410.16184**Yantao Liu, Zijun Yao, Rui Min, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-rm-bench-benchmarking-reward-models/) 6. [**RM-R1: Reward Modeling as Reasoning arXiv:2505.02387**Xiusi Chen, Gaotang Li, Ziqi Wang, et al.*2025 · 49 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-rm-r1-reward-modeling-reasoning/) 7. **[Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/guo-2025-do-large-language-models-have/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanzhu Guo, Simone Conia, Zelin Zhou, et al.*2025 · 25 citations* 8. **[Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs](https://md.research.elamur.ai/free/en/papers/yang-2025-confidence-v-s-critique-decomposition/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhe Yang, Yichang Zhang, Yudong Wang, et al.*2025 · 11 citations* 9. **[TESS 2: A Large-Scale Generalist Diffusion Language Model](https://md.research.elamur.ai/free/en/papers/tae-2025-tess-2-large-scale-generalist/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jaesung Tae, Hamish Ivison, Sachin Kumar, et al.*2025 · 9 citations* 10. [**Evaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities arXiv:2507.01479**Yingqiang Gao, Kaede Johnson, David Froehlich, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/gao-2025-evaluating-effectiveness-direct-preference-optimization/) 11. **[MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training](https://md.research.elamur.ai/free/en/papers/huang-2025-musc-improving-complex-instruction-following/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Huang, Jiaheng Liu, Yancheng He, et al.*2025 · 4 citations* 12. **[Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training](https://md.research.elamur.ai/free/en/papers/yang-2026-data-mixing-agent-learning-re/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kailai Yang, Xiao Liu, Lei Ji, et al.*2026 · 1 citation* 13. **[Exploiting Tree Structure for Credit Assignment in Reinforcement Learning with Large Language Models](https://md.research.elamur.ai/free/en/papers/tran-2026-exploiting-tree-structure-credit-assignment/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hieu Tran, Zonghai Yao, Hong yu*2026* 30 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/) — Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback