Jubayer Ibn Hamid

I am a 1st year PhD student at Stanford University. I work in artificial intelligence with a focus on algorithms for sequential decision making and reinforcement learning.

I am affiliated with Stanford Artificial Intelligence Laboratory (SAIL), where my research is advised by Dorsa Sadigh.

Previously, I studied mathematical physics as an undergraduate at Stanford University. Outside of AI, I am interested in pure mathematics, especially abstract algebra and neighbouring fields.

Research

I work on reinforcement learning and sequential decision-making with the goal of developing AI that can solve extremely complex problems in novel ways. These days, I am interested in exploration-exploitation strategies, and training-time and inference-time search.

Selected Papers

Set Reinforcement Learning: Principles and Approaches. Jubayer Ibn Hamid, Ifdita Hasan Orney, Dorsa Sadigh, Chelsea Finn, Noah Goodman. (Draft, work in progress)
Poly-EPO: Training Exploratory Reasoning Models. Ifdita Hasan Orney*, Jubayer Ibn Hamid*, Shreya Ramanujam, Shirley Wu, Hengyuan Hu, Noah Goodman, Dorsa Sadigh, Chelsea Finn. Preprint (under submission).
Polychromic Objectives for Reinforcement Learning. Jubayer Ibn Hamid*, Ifdita Hasan Orney*, Ellen Xu, Chelsea Finn, Dorsa Sadigh. International Conference on Learning Representations (ICLR), 2026.
Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling. Yuejiang Liu*, Jubayer Ibn Hamid*, Annie Xie, Yoonho Lee, Max Du, Chelsea Finn. International Conference on Learning Representations (ICLR), 2025.

(* denotes co-lead)

Full list of publications can be found here: All Papers.

Notes

Introductory notes on various topics I find interesting.

Category Theory and Algebraic Geometry. (in progress)
Abstract Algebra. (in progress)
Whitney's Embedding Theorem and Immersion Theorem.
Deep Reinforcement Learning.
Trust Region Optimization Methods.

Blogs and Visualizations

Polychromic Advantage versus Standard Advantage under Exploration Bonus.

Teaching

CS 224R - Deep Reinforcement Learning. Head CA. Spring, 2025.

CS 229 - Machine Learning. CA. Winter, 2025.