Fahim Tajwar

I am a PhD Student at the Machine Learning Department of Carnegie Mellon University. I am fortunate to be co-advised by Prof. Ruslan Salakhutdinov and Prof. Jeff Schneider.

Previously, I obtained my BS (with distinction, Mathematics) in 2022 and MS (Computer Science) in 2023 from Stanford University. There I am grateful to have my research supervised by Prof. Chelsea Finn. I am also fortunate to have worked with Prof. Percy Liang, Prof. Stefano Ermon, and Prof. Stephen Luby during my time at Stanford.

Feel free to reach out to me in case you have any questions or want to chat about my work!

Email  /  CV  /  Google Scholar  /  Github  /  LinkedIn

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Conference Publications
Maximum Likelihood Reinforcement Learning
Fahim Tajwar*, Guanning Zeng*, Yueer Zhou, Yuda Song, Daman Arora, Yiding Jiang, Jeff Schneider, Ruslan Salakhutdinov, Haiwen Feng, and Andrea Zanette
International Conference on Machine Learning (ICML), 2026 (Oral)
Workshop on Scaling Post-training for LLMs (SPOT) @ ICLR , 2026 (Best Paper Award)
[Paper], [Code], [Project Website]
Expanding the Capabilities of Reinforcement Learning via Text Feedback
Yuda Song*, Lili Chen*, Fahim Tajwar, Rémi Munos, Deepak Pathak, Drew Bagnell, Aarti Singh, and Andrea Zanette
International Conference on Machine Learning (ICML), 2026
Workshop on Lifelong Agents: Learning, Aligning, Evolving (LLA) @ ICLR , 2026 (Outstanding Paper Award)
[Paper], [Code], [Project Website]
Reasoning as an Adaptive Defense for Safety
Taeyoun Kim, Fahim Tajwar, Aditi Raghunathan, and Aviral Kumar
Neural Information Processing Systems (NeurIPS), 2025
[Paper], [Code], [Project Website]
Training a Generally Curious Agent
Fahim Tajwar*, Yiding Jiang*, Abitha Thankaraj, Sumaita Sadia Rahman, J Zico Kolter, Jeff Schneider, and Ruslan Salakhutdinov
International Conference on Machine Learning (ICML), 2025 (Oral)
[Paper], [Code], [Project Website]
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]
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]
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]
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]
Journal Publications
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]
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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