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Evan Frick (born July 17, 2002) is an American machine learning researcher and computer scientist known for his contributions to the evaluation and post-training of large language models (LLMs). He is a researcher at Arena.ai and a contributor to the LMSYS Org (Large Model Systems Organization), specifically working on the Chatbot Arena benchmarking platform.^[1]

Frick attended the University of California, Berkeley, where he earned a B.A. in Computer Science (2024) and an M.S. in Electrical Engineering and Computer Science (2025). During his graduate studies, he conducted research on reward modeling and reinforcement learning from human feedback (RLHF) under the supervision of Jiantao Jiao and in collaboration with Ion Stoica.^[2]

Frick’s research focuses on the "post-training" phase of AI development, specifically how to align model outputs with human preferences. As a member of the LMSYS Org team, he co-developed Arena-Hard and the Prompt-to-Leaderboard (P2L) system, which aim to provide more nuanced evaluations than traditional static benchmarks.^[3]

He has held positions at:

Arena.ai: Member of Technical Staff focusing on preference modeling. Nexusflow: Machine Learning Engineer, where he contributed to the development of the "Athene" series of open-weights models. Berkeley AI Research (BAIR): Research contributor to the Starling-7B project, a model series utilizing Reinforcement Learning from AI Feedback (RLAIF). His work has been published in machine learning conferences including the International Conference on Machine Learning (ICML) and the International Conference on Learning Representations (ICLR).^[4]

Frick, E., et al. (2025). "Prompt-to-Leaderboard: Prompt-Adaptive LLM Evaluations." Proceedings of the 42nd ICML. Zhu, B., Frick, E., et al. (2024). "Starling-7B: Improving Helpfulness and Harmlessness with RLAIF." Conference on Language Modeling (COLM). Lambert, N., ... Frick, E., et al. (2024). "How to Evaluate Reward Models for RLHF." ICLR 2024.

Official website Evan Frick publications indexed by Google Scholar