--- title: "Who cites ODIN: Disentangled Reward — 37 citing papers" description: "37 papers of the Elamur Research library cite «ODIN: Disentangled Reward Mitigates Hacking in RLHF», found in their bibliographies; the…" canonical: "https://research.elamur.ai/free/en/papers/chen-2024-odin-disentangled-reward-mitigates-hacking/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chen-2024-odin-disentangled-reward-mitigates-hacking/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [ODIN: Disentangled Reward Mitigates Hacking in…](https://md.research.elamur.ai/free/en/papers/chen-2024-odin-disentangled-reward-mitigates-hacking/) / cited by # Who cites «ODIN: Disentangled Reward Mitigates Hacking in RLHF» 37 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · [← back to «ODIN: Disentangled Reward Mitigates Hacking in RLHF»](https://md.research.elamur.ai/free/en/papers/chen-2024-odin-disentangled-reward-mitigates-hacking/) 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. **[Disentangling Length from Quality in Direct Preference Optimization](https://md.research.elamur.ai/free/en/papers/park-2024-disentangling-length-quality-direct-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ryan Park, Rafael Rafailov, Stefano Ermon, et al.*2024 · 105 citations* 3. [**Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving arXiv:2502.07640**Yong Lin, Shange Tang, Bohan Lyu, et al.*2025 · 84 citations*](https://md.research.elamur.ai/free/en/papers/lin-2025-goedel-prover-frontier-model-open/) 4. [**Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains arXiv:2507.17746**Anisha Gunjal, Anthony Wang, Elaine Lau, et al.*2025 · 73 citations*](https://md.research.elamur.ai/free/en/papers/gunjal-2025-rubrics-rewards-reinforcement-learning-verifiable/) 5. **[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* 6. [**SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models arXiv:2504.11468**Hardy Chen, Haoqin Tu, Fali Wang, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-sft-rl-early-investigation-training/) 7. **[Following Length Constraints in Instructions](https://md.research.elamur.ai/free/en/papers/yuan-2025-following-length-constraints-instructions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weizhe Yuan, Ilia Kulikov, Ping Yu, et al.*2025 · 22 citations* 8. [**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/) 9. **[JuStRank: Benchmarking LLM Judges for System Ranking](https://md.research.elamur.ai/free/en/papers/gera-2025-justrank-benchmarking-llm-judges-system/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ariel Gera, Odellia Boni, Yotam Perlitz, et al.*2025 · 15 citations* 10. [**Rethinking Rubric Generation for Improving LLM Judge and Reward Modeling for Open-ended Tasks arXiv:2602.05125**William F. Shen, Xinchi Qiu, Chenxi Whitehouse, et al.*2026 · 9 citations*](https://md.research.elamur.ai/free/en/papers/shen-2026-rethinking-rubric-generation-improving-llm/) 11. **[Bias Fitting to Mitigate Length Bias of Reward Model in RLHF](https://md.research.elamur.ai/free/en/papers/zhao-2026-bias-fitting-mitigate-length-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, et al.*2026 · 2 citations* 12. **[HARM: Learning Hate-Aware Reward Model for Evaluating Natural Language Explanations of Offensive Content](https://md.research.elamur.ai/free/en/papers/vecchi-2026-harm-learning-hate-aware-reward/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lorenzo Puppi Vecchi, Alceu de Souza Britto Jr, Emerson Cabrera Paraiso, et al.*2026 · 1 citation* 13. **[Debiasing Reward Models via Causally Motivated Inference-Time Intervention](https://md.research.elamur.ai/free/en/papers/shinoda-2026-debiasing-reward-models-causally-motivated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kazutoshi Shinoda, Kosuke Nishida, Kyosuke Nishida*2026* 14. **[InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization](https://md.research.elamur.ai/free/en/papers/nishida-2026-instructsum-benchmark-evaluate-instruction-following/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kosuke Nishida, Kyosuke Nishida, Itsumi Saito*2026* 15. **[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* 16. **[WildReward: Learning Reward Models from In-the-Wild Human Interactions](https://md.research.elamur.ai/free/en/papers/peng-2026-wildreward-learning-reward-models-wild/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Peng, Yunjia Qi, Xiaozhi Wang, et al.*2026* 21 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/chen-2024-odin-disentangled-reward-mitigates-hacking/) — ODIN: Disentangled Reward Mitigates Hacking in RLHF