Mehul Damani

Hello! I am a third year Ph.D. student at MIT advised by Jacob Andreas.

My research interests lie at the intersection of reinforcement learning (RL) and large language models (LLMs). I am very excited by the potential of RL to improve reasoning, math, coding, and other capabilities in LLMs.Currently, I am thinking about how RL can be used to improve calibration and reduce hallucinations in LLMs. Finally, I have also been thinking about the paradigm of inference-time compute, and how optimally selecting inference-time techniques can significantly improve the efficiency of LLMs.

Previously, I worked with Lerrel Pinto at NYU on developing automatic curriculum learning methods for RL agents. Before that, I was a part of the MARMot Lab at NUS, where I worked with Guillaume Sartoretti on applying multi-agent reinforcement learning to traffic signal control and multi-agent pathfinding.

I’m always excited to explore new research directions and am open to collaborating or advising students. If you are interested in my research or simply want to chat, don’t hesitate to get in touch!

Selected Publications

  1. ICML
    The Surprising Effectiveness of Test-Time Training for Abstract Reasoning
    Akyürek, Ekin, Damani, Mehul, Qiu, Linlu, Guo, Han, Kim, Yoon, and Andreas, Jacob
    International Conference on Machine Learning 2025
    arXiv
  2. ICLR
    Learning How Hard to Think: Input-Adaptive Allocation of LM Computation
    Damani, Mehul, Shenfeld, Idan, Peng, Andi, Bobu, Andreea, and Andreas, Jacob
    International Conference on Learning Representations 2025
    arXiv
  3. TMLR
    Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
    Casper, Stephen, Davies, Xander, Shi, Claudia, Gilbert, Thomas Krendl, Scheurer, Jeremey, Rando, Javier, Freedman, Rachel, Korbak, Tomasz, Lindner, David, Freire, Pedro, Wang, Tony Tong, Marks, Samuel, Segerie, Charbel-Raphael, Carroll, Micah, Peng, Andi, Christoffersen, Phillip, Damani, Mehul, Slocum, Stewart, Anwar, Usman, Siththaranjan, Anand, Nadeau, Max, Michaud, Eric J, Pfau, Jacob, Krasheninnikov, Dmitrii, Chen, Xin, Langosco, Lauro, Hase, Peter, Biyik, Erdem, Dragan, Anca, Krueger, David, Sadigh, Dorsa, and Hadfield-Menell, Dylan
    Transactions on Machine Learning Research 2023
    arXiv
  4. IEEE-RAL, ICRA
    PRIMAL2: Pathfinding Via Reinforcement and Imitation Multi-Agent Learning - Lifelong
    Damani, Mehul, Luo, Zhiyao, Wenzel, Emerson, and Sartoretti, Guillaume
    IEEE Robotics and Automation Letters 2021
    arXiv PDF Code