Saket Tiwari

Saket Tiwari

6th Year PhD student at Brown (on job market for PostDoc)

Publications

From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments

Published in ICLR, 2026

We present a novel theoretical framework for deep RL in continuous environments by modeling the problem as a continuous-time stochastic process, deriving equations describing how the state distribution evolves over gradient steps in the infinite width limit.

Recommended citation: Saket Tiwari, Tejas Kotwal, & George Konidaris. (2026). "From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments." arXiv:2606.04275 https://arxiv.org/abs/2606.04275

Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning

Published in ICML, 2026

We show that loss of plasticity in deep continual learning is preceded by Hessian spectral collapse, and introduce tau-trainability as a unifying framework, with regularization enhancements that effectively preserve plasticity.

Recommended citation: Naicheng He, Kaicheng Guo, Arjun Prakash, Saket Tiwari, Ruo Yu Tao, Tyrone Serapio, Amy Greenwald, & George Konidaris. (2025). "Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning." arXiv:2509.22335 https://arxiv.org/abs/2509.22335

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces

Published in ICLR [ORAL: top 1.8% of submitted], 2025

We prove that the state space is a low dimensional manifold for reinforcement learning in the infinite width limit of two layer neural networks and utilise this to improve performance in dog and humanoid environments.

Recommended citation: Saket Tiwari, Omer Gottesman, & George Konidaris. (2025). "Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces." ICLR 2025 https://openreview.net/pdf?id=AP0ndQloqR

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Published in Neural Networks Journal, 2023

Provided various benchmarking results and methodologies for lifelong reinforcement learning

Recommended citation: Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha. (2023). "A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems" Neural Networks Volume 160, 2023. https://www.sciencedirect.com/science/article/abs/pii/S0893608023000072

Natural Option Critic

Published in AAAI, 2019

We derive a practical natural gradient method for the option critic framework in Hierarchical Reinforcement Learning

Recommended citation: Saket Tiwari, & Philip Thomas. (2019). "Natural Option Critic." AAAI 2019 https://arxiv.org/pdf/1812.01488.pdf