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References (48)

[1]
Reinforcement Learning with Rubric Anchors
2025Zenan Huang, Yihong Zhuang et al.
[2]
Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
2025Anisha Gunjal, Anthony Wang et al.
[3]
BLEUBERI: BLEU is a surprisingly effective reward for instruction following
2025Yapei Chang, Yekyung Kim et al.
[4]
Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data
2025Shuai Zhao, Linchao Zhu et al.
[5]
No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding
2025Michael Krumdick, Charles Lovering et al.
[6]
RewardBench: Evaluating Reward Models for Language Modeling
2025Nathan Lambert, Valentina Pyatkin et al.
[7]
HREF: Human Response-Guided Evaluation of Instruction Following in Language Models
2024Xinxi Lyu, Yizhong Wang et al.
[8]
Qwen2.5 Technical Report
2024Qwen An Yang, Baosong Yang et al.
[9]
TÜLU 3: Pushing Frontiers in Open Language Model Post-Training
2024Nathan Lambert, Jacob Daniel Morrison et al.
[10]
GPT-4o System Card
2024OpenAI Aaron Hurst, Adam Lerer et al.
[11]
How to Evaluate Reward Models for RLHF
2024Evan Frick, Tianle Li et al.
[12]
RMB: Comprehensively Benchmarking Reward Models in LLM Alignment
2024Enyu Zhou, Guodong Zheng et al.
[13]
RevisEval: Improving LLM-as-a-Judge via Response-Adapted References
2024Qiyuan Zhang, Yufei Wang et al.
[14]
Self-rationalization improves LLM as a fine-grained judge
2024Prapti Trivedi, Aditya Gulati et al.
[15]
Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge
2024Jiayi Ye, Yanbo Wang et al.
[16]
Generative Reward Models
2024Dakota Mahan, Duy Phung et al.
[17]
Generative Verifiers: Reward Modeling as Next-Token Prediction
2024Lunjun Zhang, Arian Hosseini et al.
[18]
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
2024Tianhao Wu, Weizhe Yuan et al.
[19]
OffsetBias: Leveraging Debiased Data for Tuning Evaluators
2024Junsoo Park, Seungyeon Jwa et al.
[20]
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts
2024Haoxiang Wang, Wei Xiong et al.

Showing 20 of 48 references

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