Glen Berseth

alexandre.brown@mila.quebec

Biography

Glen Berseth is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute.

He is a Canada CIFAR AI Chair and co-directs the Robotics and Embodied AI Lab (REAL). He was formerly a postdoctoral researcher at Berkeley Artificial Intelligence Research (BAIR), working with Sergey Levine.

Berseth’s previous and current research has focused on solving sequential decision-making problems (planning) for real-world autonomous learning systems (robots). More specifically, his research has focused on human-robot collaboration, reinforcement, and continual-, meta-, multi-agent and hierarchical learning.

He has published in the top venues in robotics, machine learning and computer animation. He teaches a course on robot learning at Université de Montréal and at Mila, in which he covers the most recent research on machine learning techniques for creating generalist robots.

Current Students

Özgür Aslan

PhD - Université de Montréal

ozgur.aslan@mila.quebec

PhD - Université de Montréal

Master's Research - Université de Montréal

charlotte.cloutier@mila.quebec

Roger Creus-Castanyer

PhD - Université de Montréal

Co-supervisor :

Pablo Samuel Castro

roger.creus-castanyer@mila.quebec

PhD - McGill University

Principal supervisor :

Hsiu-Chin Lin

elham.daneshmand@mila.quebec

Léa Demeule

PhD - Université de Montréal

Co-supervisor :

Chris Pal

lea.demeule@mila.quebec

homayoun.honari@mila.quebec

Homayoun Honari

Collaborating researcher - Université de Montréal

Naser Kazemi

Collaborating researcher - EPFL

naser.kazemi@mila.quebec

Adriana Knatchbull-Hugessen

PhD - Université de Montréal

adriana.knatchbull-hugessen@mila.quebec

artur.kuramshin@mila.quebec

Artur Kuramshin

Master's Research - Université de Montréal

daniel.lawson@mila.quebec

Daniel Lawson

PhD - Université de Montréal

Co-supervisor :

Khimya Khetarpal

Postdoctorate - Université de Montréal

tabitha-edith.lee@mila.quebec

PhD - Université de Montréal

faisal.mohamed@mila.quebec

Research Intern - Université de Montréal

josuesmjr.mongan@gmail.com

michael.przystupa@mila.quebec

Michael Przystupa

Postdoctorate - Université de Montréal

Esra'a Saleh

PhD - Université de Montréal

Co-supervisor :

Aaron Courville

esraa.saleh@mila.quebec

jesse.silverberg@mila.quebec

Jesse Silverberg

PhD - Université de Montréal

Co-supervisor :

Marc Gendron-Bellemare

PhD - Université de Montréal

Co-supervisor :

Yoshua Bengio

siddarth.venkatraman@mila.quebec

Real-time Reinforcement Learning

Albert Zhan

PhD - Université de Montréal

albert.zhan@mila.quebec

Blog Posts

Deux robots dans une cuisine, en train de préparer le dîner. L'un coupe les légumes et l'autre fait une omelette.

June 20, 2025

Ivan Anokhin

Matthew Riemer

Rishav Rishav

Gopeshh Subbaraj

Glen Berseth

Read the article

February 15, 2022

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation Primary tabs View Edit(active tab) Delete Revisions

Jędrzej Orbik

Charles Sun

Coline Devin

Glen Berseth

Read the article

Publications

RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies

Pranav Atreya

Karl Pertsch

Tony Lee

Moo Jin Kim

Arhan Jain

Artur Kuramshin

Cyrus Neary

Edward S. Hu

Kanav Arora

Kirsty Ellis

Luca Macesanu

Matthew Leonard

Meedeum Cho

Özgür Aslan

Shivin Dass

Tony Wang

Xingfang Yuan

Abhishek Gupta

Dinesh Jayaraman

Glen Berseth … (see 6 more)

Kostas Daniilidis

Roberto Martín-Martín

Youngwoon Lee

Percy Liang

Chelsea Finn

Sergey Levine

2025-10-06

Proceedings of The 8th Conference on Robot Learning (published)

proceedings.mlr.press

Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models

Johan Samir Obando Ceron

Yoshua Bengio

Brian R. Bartoldson

Bhavya Kailkhura

Guillaume Lajoie

Nikolay Malkin

Moksh Jain

Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference… (see more) to make a prediction. Inference-time compute can be scaled in parallel by choosing among multiple independent solutions or sequentially through self-refinement. We propose Recursive Self-Aggregation (RSA), a test-time scaling method inspired by evolutionary methods that combines the benefits of both parallel and sequential scaling. Each step of RSA refines a population of candidate reasoning chains through aggregation of subsets to yield a population of improved solutions, which are then used as the candidate pool for the next iteration. RSA exploits the rich information embedded in the reasoning chains -- not just the final answers -- and enables bootstrapping from partially correct intermediate steps within different chains of thought. Empirically, RSA delivers substantial performance gains with increasing compute budgets across diverse tasks, model families and sizes. Notably, RSA enables Qwen3-4B-Instruct-2507 to achieve competitive performance with larger reasoning models, including DeepSeek-R1 and o3-mini (high), while outperforming purely parallel and sequential scaling strategies across AIME-25, HMMT-25, Reasoning Gym, LiveCodeBench-v6, and SuperGPQA. We further demonstrate that training the model to combine solutions via a novel aggregation-aware reinforcement learning approach yields significant performance gains. Code available at https://github.com/HyperPotatoNeo/RSA.

2025-09-29

ArXiv (preprint)

doi.org

arxiv.org

Concept-based Steering of Large Language Models for Conditional Molecular Generation

Yang Zhang

Modern LLMs, with their internet-scale pretraining and advanced human-level capabilities across specialized tasks, have demonstrated promisi… (see more)ng performance in molecular discovery using existing text-based molecular representations, such as SMILES and SELFIES. However, generating valid, unique, and high-fidelity molecules while precisely controlling for multiple properties simultaneously remains challenging. While prior works demonstrated success by fine-tuning language models on a novel corpus of molecules with property-conditioned tags, real-world applications require generating molecules from diverse property distributions, previously unseen in the training data. To this end, we present Concept-based Activation STeering (CAST), the first approach to apply activation steering to directly edit a model's internal representation for conditional molecular generation. CAST offers a lightweight, flexible alternative to fine-tuning by computing property-conditioned steering vectors via a concept network that does not require retraining the LLM. Through extensive experiments on datasets such as Therapeutics Data Commons, we show that CAST consistently outperforms existing methods on both in-distribution and out-of-distribution conditional generation tasks. We also conduct comprehensive ablation studies to highlight the extent of control our concept-guided steering provides on the molecules generated by the LLM.

2025-09-19

NeurIPS.cc/2025/Workshop/AI4Mat (poster)

Task Robustness via Re-Labelling Vision-Action Robot Data

Artur Kuramshin

Özgür Aslan

Cyrus Neary

2025-09-05

robot-learning.org/CoRL/2025/Workshop/Robot_Data (published)

Adaptive Resolution Residual Networks — Generalizing Across Resolutions Easily and Efficiently

Léa Demeule

Mahtab Sandhu

The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, most deep learning arc… (see more)hitectures are fixed-resolution; they consider a single resolution at training and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. In contrast, other deep learning architectures are adaptive-resolution; they directly allow various resolutions to be processed at training and inference time. This provides computational adaptivity but either sacrifices robustness or compatibility with mainstream layers, which hinders their use. In this work, we introduce Adaptive Resolution Residual Networks (ARRNs) to surpass this tradeoff. We construct ARRNs from Laplacian residuals, which serve as generic adaptive-resolution adapters for fixed-resolution layers. We use smoothing filters within Laplacian residuals to linearly separate input signals over a series of resolution steps. We can thereby skip Laplacian residuals to cast high-resolution ARRNs into low-resolution ARRNs that are computationally cheaper yet numerically identical over low-resolution signals. We guarantee this result when Laplacian residuals are implemented with perfect smoothing kernels. We complement this novel component with Laplacian dropout, which randomly omits Laplacian residuals during training. This regularizes for robustness to a distribution of lower resolutions. This also regularizes for numerical errors that may occur when Laplacian residuals are implemented with approximate smoothing kernels. We provide a solid grounding for the advantageous properties of ARRNs through a theoretical analysis based on neural operators, and empirically show that ARRNs embrace the challenge posed by diverse resolutions with computational adaptivity, robustness, and compatibility with mainstream layers.

2025-07-16

Transactions on Machine Learning Research (accepted)

doi.org

Curiosity-Driven Exploration via Temporal Contrastive Learning

Faisal Mohamed

Catherine Ji

Benjamin Eysenbach

Effective exploration in reinforcement learning requires keeping track not just of where the agent has been, but also of how the agent think… (see more)s about and represents the world: an agent should explore states that enable it to learn powerful representations. Temporal representations can include the information required to solve any potential task while avoiding the computational cost of reconstruction. In this paper, we propose an exploration method that uses temporal contrastive representations to drive exploration, maximizing coverage as seen through the lens of these temporal representations. We demonstrate complex exploration behaviors in locomotion, manipulation, and embodied-AI tasks, revealing previously unknown capabilities and behaviors once achievable only via extrinsic rewards.

2025-06-30

rl-conference.cc/RLC/2025/Workshop/RLBrew (published)

Is Exploration or Optimization the Problem for Deep Reinforcement Learning?

2025-06-30

rl-conference.cc/RLC/2025/Workshop/Finding_the_Frame (published)

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning

Adriana Hugessen

Behavioral cloning (BC) methods trained with supervised learning (SL) are an effective way to learn policies from human demonstrations in do… (see more)mains like robotics. Goal-conditioning these policies enables a single generalist policy to capture diverse behaviors contained within an offline dataset. While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require conditioning on novel state-goal pairs, i.e. combinatorial generalization. In part, this limitation can be attributed to a lack of temporal consistency in the state representation learned by BC; if temporally related states are encoded to similar latent representations, then the out-of-distribution gap for novel state-goal pairs would be reduced. Hence, encouraging this temporal consistency in the representation space should facilitate combinatorial generalization. Successor representations, which encode the distribution of future states visited from the current state, nicely encapsulate this property. However, previous methods for learning successor representations have relied on contrastive samples, temporal-difference (TD) learning, or both. In this work, we propose a simple yet effective representation learning objective,

2025-06-30

rl-conference.cc/RLC/2025/Workshop/RLBrew (published)

doi.org

What Matters for Maximizing Data Reuse In Value-based Deep Reinforcement Learning

Roger Creus Castanyer

Pablo Samuel Castro

A key ingredient for successfully applying deep reinforcement learning to challenging tasks is the effective use of data at scale. Although … (see more)originally deep RL algorithms achieved this by storing past experiences collected from a synchronous actor in an external replay memory [DQN; Mnih et al., 2013], follow-up works scaled training by collecting data asynchronously through distributed actors [R2D2; Kapturowski et al., 2018], and more recently by GPU-optimized parallelization [PQN; Gallici et al., 2024]. We argue that DQN, PQN, and R2D2 constitute a group of value-based methods for parallel training and study them to shed light on the dynamics induced by varying data collection schemes. We conduct a thorough empirical study to better understand these dynamics, and propose the Data Replay Ratio as a novel metric for quantifying data reuse. Our findings suggest that maximizing data reuse involves directly addressing the deadly triad: Q-lambda rollouts for reducing the bias from bootstrapping, the use of LayerNorm for stabilizing function approximation, and parallelized data collection for mitigating off-policy divergence.

2025-06-30

rl-conference.cc/RLC/2025/Workshop/Finding_the_Frame (published)

Zero-Shot Constraint Satisfaction with Forward- Backward Representations

Adriana Hugessen

Harley Wiltzer

Cyrus Neary

Amy Zhang

Traditionally, constrained policy optimization with Reinforcement Learning (RL) requires learning a new policy from scratch for any new envi… (see more)ronment, goal or cost function, with limited generalization to new tasks and constraints. Given the sample inefficiency of many common deep RL methods, this procedure can be impractical for many real-world scenarios, particularly when constraints or tasks are changing. As an alternative, in the unconstrained setting, various works have sought to pre-train representations from offline datasets to accelerate policy optimization upon specification of a reward. Such methods can permit faster adaptation to new tasks in a given environment, dramatically improving sample efficiency. Recently, zero-shot policy optimization has been explored by leveraging a particular

2025-06-30

rl-conference.cc/RLC/2025/Workshop/RLBrew (published)

Scalable Tree Search over Graphs with Learned Action Pruning for Power Grid Control

Florence Cloutier

Cyrus Neary

Adriana Hugessen

Viktor Todosijevic

Zina Kamel

As real-world infrastructure systems become increasingly complex and large-scale, there is a growing need for learning-based control strateg… (see more)ies that can make informed decisions in complex and dynamic environments. However, large-scale problems — such as power grid control — introduce high-dimensional action spaces and necessitate transferability across varying grid topologies. We introduce **H**ierarchical **E**xpert-Guided **R**econfiguration **O**ptimization for **G**raph **T**opologies, **HERO-GT**, a model-based planning approach that combines a pretrained graph neural network (GNN) for topology-aware action pruning with a Monte Carlo Tree Search (MCTS) planner for targeted, structured exploration. More specifically, the high-level GNN predicts a promising subset of actions, which the low-level MCTS agent uses to focus its search and reduce computational overhead while remaining adaptable to unseen graph structures. Furthermore, the MCTS planner leverages a given *default policy*---which may be defined, for example, by heuristics, problem relaxations, or rule-based methods---to bias the search and prioritize actions that are expected to improve performance over the default. We deploy HERO-GT in power grid environments, demonstrating that it not only improves over a strong default policy, but also scales to a realistic operational setting where exhaustive search becomes computationally infeasible.

2025-06-16

rl-conference.cc/RLC/2025/Workshop/RL4RS (published)

Exploration by Exploitation: Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search

Tabitha Edith Lee

Nan Rosemary Ke

Sarvesh Patil

Annya Dahmani

Eunice Yiu

Esra'a Saleh

Alison Gopnik

Oliver Kroemer

2025-06-11

ICML.cc/2025/Workshop/EXAIT (poster)