Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Fahim Tajwar*,
Anikait Singh*,
Archit Sharma,
Rafael Rafailov,
Jeff Schneider,
Tengyang Xie,
Stefano Ermon,
Chelsea Finn, and
Aviral Kumar
International Conference on Machine Learning (ICML), 2024
[Paper],
[Code],
[Project Website]
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Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
Yoonho Lee*,
Annie S Chen*,
Fahim Tajwar,
Ananya Kumar,
Huaxiu Yao,
Percy Liang, and
Chelsea Finn
International Conference on Learning Representations (ICLR), 2023
[Paper],
[Code]
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When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
Annie Xie*,
Fahim Tajwar*,
Archit Sharma*, and
Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2022
[Paper],
[Code],
[Project Website]
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Do Deep Networks Transfer Invariances Across Classes?
Allan Zhou*,
Fahim Tajwar*,
Alexander Robey,
Tom Knowles,
George J Pappas,
Hamed Hassani, and
Chelsea Finn
International Conference on Learning Representations (ICLR), 2022
[Paper],
[Code]
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Conservative Prediction via Data-Driven Confidence Minimization
Caroline Choi*,
Fahim Tajwar*,
Yoonho Lee*,
Huaxiu Yao,
Ananya Kumar, and
Chelsea Finn
Transactions on Machine Learning Research (TMLR), 2024
[Paper],
[Code]
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Scalable deep learning to identify brick kilns and aid regulatory capacity
Jihyeon Lee*,
Nina R. Brooks*,
Fahim Tajwar,
Marshall Burke,
Stefano Ermon,
David B. Lobell,
Debashish Biswas, and
Stephen Luby
Proceedings of the National Academy of Sciences (PNAS), 2021
[Paper],
[Code]
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Training a Generally Curious Agent
Fahim Tajwar*,
Yiding Jiang*,
Abitha Thankaraj,
Sumaita Sadia Rahman,
J Zico Kolter,
Jeff Schneider, and
Ruslan Salakhutdinov
Preprint, 2025
[Paper],
[Code],
[Project Website]
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Website template
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