Sarath Chandar

istabrak.abbes@mila.quebec

Biography

Sarath Chandar is an associate professor at Polytechnique Montreal's Department of Computer and Software Engineering, where he leads the Chandar Research Lab. He is also a Core Academic Member at Mila – Quebec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair and the Canada Research Chair in Lifelong Machine Learning.

Chandar’s research interests include lifelong learning, deep learning, optimization, reinforcement learning and natural language processing. To promote research in lifelong learning, Chandar created the Conference on Lifelong Learning Agents (CoLLAs) in 2022, for which he served as program chair in 2022 and 2023.

He has a PhD from Université de Montréal and an MSc (By Research) from the Indian Institute of Technology Madras.

Current Students

Istabrak Abbes

PhD - Université de Montréal

Alex Aselstyne

Master's Research - Polytechnique Montréal

alexander.aselstyne@mila.quebec

davide.baldelli@mila.quebec

Davide Baldelli

PhD - Polytechnique Montréal

Co-supervisor :

Amal Zouaq

diego.cerda-mardini@mila.quebec

Milan Bhan

Collaborating researcher

milan.bhan@mila.quebec

Diego Cerda Mardini

Master's Research - McGill University

Prashant Govindarajan

PhD - Polytechnique Montréal

prashant.govindarajan@mila.quebec

nathanael.haas@mila.quebec

Simon Guiroy

PhD - Université de Montréal

Principal supervisor :

Research Intern - Polytechnique Montréal

Maryam Hashemzadeh

PhD - Université de Montréal

maryam.hashemzadeh@mila.quebec

david.heurtel-depeiges@mila.quebec

David Heurtel--Depeiges

PhD - Polytechnique Montréal

Jerry Huang

PhD - Université de Montréal

jerry.huang@mila.quebec

Kanishk Jain

PhD - Université de Montréal

Co-supervisor :

Ross Goroshin

kanishk.jain@mila.quebec

Saurav Jha

Postdoctorate - Polytechnique Montréal

saurav.jha@mila.quebec

Amir Ardalan Kalantari Dehaghi

PhD - Université de Montréal

Master's Research - Polytechnique Montréal

antoine.lafrance@mila.quebec

lola.lebreton@mila.quebec

Lola Le Breton

PhD - Polytechnique Montréal

Tom Lee

Postdoctorate - Polytechnique Montréal

thomas.lee@mila.quebec

ekaterina.lobacheva@mila.quebec

Aidan Li

Master's Research - Université de Montréal

Co-supervisor :

Postdoctorate - Université de Montréal

PhD - Polytechnique Montréal

mansi.maheshwari@mila.quebec

Roshan Munirathinam Sankaran Balaji

Collaborating researcher - Polytechnique Montréal

roshan.munirathinam@mila.quebec

nilaksh.nilaksh@mila.quebec

Hadi NekoeiQachkanloo

PhD - Université de Montréal

nekoeihe@mila.quebec

Nilaksh Nilaksh

PhD - Polytechnique Montréal

PhD - Université de Montréal

darshan.patil@mila.quebec

Linda Peinthiere

Collaborating researcher - Polytechnique Montréal Montreal

uphro_1@hotmail.com

mathieu.reymond@mila.quebec

Yann Pernot

Master's Research - Polytechnique Montréal

yann.pernot@mila.quebec

Mathieu Reymond

Collaborating Alumni

Shaipranesh Senthilkumar

PhD - Polytechnique Montréal

shaipraneshgci@gmail.com

nishant.sharma@mila.quebec

Nour Shaheen

Master's Research - Polytechnique Montréal

Principal supervisor :

Amine Mhedhbi

nour.shaheen@mila.quebec

Master's Research - Université de Montréal

Co-supervisor :

Amal Zouaq

Arjun Vaithilingam Sudhakar

Achille Sowa Samo

PhD - Polytechnique Montréal

achille.sowa@mila.quebec

Anabel Tan

Postdoctorate - Université de Montréal

Co-supervisor :

Julie Hlavacek-Larrondo

anabel.tan@mila.quebec

Megh Thakkar

Master's Research - Université de Montréal

megh.thakkar@mila.quebec

PhD - Polytechnique Montréal

arjun.vaithilingam-sudhakar@mila.quebec

Abdelrahman Zayed

PhD - Polytechnique Montréal

zayedabd@mila.quebec

Xutong Zhao

PhD - Polytechnique Montréal

xutong.zhao@mila.quebec

Artem Zholus

PhD - Polytechnique Montréal

artem.zholus@mila.quebec

Improving CAD Design With LLMs

Zayd Zuhri

Master's Research - Université de Montréal

Co-supervisor :

Blog Posts

December 19, 2025

Prashant Govindarajan

Davide Baldelli

Quentin Fournier

Sarath Chandar

Read the article

A digital picture of Bert from Sesame street, wering black trench coat and sunglasses

March 3, 2025

NeoBERT: A New Frontier for Open-Source Encoder Language Models

Lola Le Breton

Quentin Fournier

Sarath Chandar

Read the article

October 1, 2024

How Do We Explain AI and Ensure the Explanation Is True? Faithfulness Measurable Models Tell You How

Andrea Madsen

Siva Reddy

Sarath Chandar

Read the article

Publications

TAPNext++: What's Next for Tracking Any Point (TAP)?

Sebastian Jung

Artem Zholus

Martin Sundermeyer

Carl Doersch

Ross Goroshin

David Joseph Tan

Rudolph Triebel

Federico Tombari

Tracking-Any-Point (TAP) models aim to track any point through a video which is a crucial task in AR/XR and robotics applications. The recen… (see more)tly introduced TAPNext approach proposes an end-to-end, recurrent transformer architecture to track points frame-by-frame in a purely online fashion -- demonstrating competitive performance at minimal latency. However, we show that TAPNext struggles with longer video sequences and also frequently fails to re-detect query points that reappear after being occluded or leaving the frame. In this work, we present TAPNext++, a model that tracks points in sequences that are orders of magnitude longer while preserving the low memory and compute footprint of the architecture. We train the recurrent video transformer using several data-driven solutions, including training on long 1024-frame sequences enabled by sequence parallelism techniques. We highlight that re-detection performance is a blind spot in the current literature and introduce a new metric, Re-Detection Average Jaccard (

2026-04-11

arXiv (preprint)

arxiv.org

Emergent Reasoning via Recursive Latent Reinforcement Pretraining

Large language models (LLMs) often rely on explicit chain-of-thought (CoT) traces to solve multi-step reasoning problems, but these traces i… (see more)ncrease inference cost, expose brittle prompt dependence, and complicate training objectives. We study an alternative: \emph{latent deliberation} implemented as a small recurrent refinement module that performs multiple internal ``thinking`` steps while keeping the external sequence length fixed. We introduce \textbf{Recursive Latent Reinforcement Pretraining (RLRP)}, a training recipe that augments a base causal LLM with a shared latent head executed for

2026-03-04

LLM_Reasoning @ International Conference on Learning Representations (published)

Is Depth Heterogeneity a Barrier to Model Merging?

Model merging offers a way to combine the capabilities of several networks at test time without retraining or additional finetuning, but mos… (see more)t merging methods assume identical architectures. Depth differences are commonly viewed as a major obstacle because they remove clear layer correspondences. We test this assumption by merging residual networks that differ only in depth, using a simple training-free pipeline based on identity expansion and permutation alignment. Across both same-task and multitask image classification experiments, heterogeneous merges closely match homogeneous ones. The results suggest that, for residual networks, depth mismatch is not the main barrier to effective model merging, and that the main difficulty in model merging comes from aligning independently trained weights in a homogeneous setting.

2026-02-28

TTU_Main_Track @ International Conference on Learning Representations (published)

Loss Smoothing for Continual Adaptation

Neural networks are often adapted in nonstationary data distributions settings where the objective is to optimize performance on the current… (see more) task, and preserving accuracy on previous tasks is not required. As a result, existing methods primarily focus on improving plasticity, while stability is largely studied in the context of continual learning. In this work, we examine whether preserving stability can also be beneficial in model adaptation settings where past-task performance is irrelevant. We propose a simple loss smoothing approach that encourages selective adaptation by preserving task-shared features while modifying task-inconsistent ones. We evaluate our method on continual supervised model adaptation benchmarks and reinforcement learning benchmarks, and show that promoting representational stability during adaptation can improve performance across settings.

2026-02-27

CAO @ International Conference on Learning Representations (poster)

Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning

Nilaksh

2026-02-09

arXiv (preprint)

arxiv.org

The Markovian Thinker: Architecture-Agnostic Linear Scaling of Reasoning

Milad Aghajohari

Kamran Chitsaz

Amirhossein Kazemnejad

Reinforcement learning (RL) has recently become a strong recipe for training reasoning LLMs that produce long chains of thought (LongCoT). Y… (see more)et the standard RL"thinking environment", where the state is the prompt plus all prior reasoning tokens, makes the state unbounded and forces attention-based policies to pay quadratic compute as thoughts lengthen. We revisit the environment itself. We propose Markovian Thinking, a paradigm in which the policy advances reasoning while conditioning on a constant-size state, decoupling thinking length from context size. As an immediate consequence this yields linear compute with constant memory. We instantiate this idea with Delethink, an RL environment that structures reasoning into fixed-size chunks. Within each chunk, the model thinks as usual; at the boundary, the environment resets the context and reinitializes the prompt with a short carryover. Through RL, the policy learns to write a textual state near the end of each chunk sufficient for seamless continuation of reasoning after reset. Trained in this environment, an R1-Distill 1.5B model reasons in 8K-token chunks yet thinks up to 24K tokens, matching or surpassing LongCoT-RL trained with a 24K budget. With test-time scaling, Delethink continues to improve where LongCoT plateaus. The effect of linear compute is substantial: we empirically estimate at 96K average thinking length LongCoT-RL costs 27 H100-months vs. 7 for Delethink. Analysis at RL initialization shows off-the-shelf reasoning models (1.5B-120B) often sample Markovian traces zero-shot across diverse benchmarks, providing positive samples that make RL effective at scale. Our results show that redesigning the thinking environment is a powerful lever: it enables very long reasoning without quadratic overhead and opens a path toward efficient, scalable reasoning LLMs.

2026-01-25

International Conference on Learning Representations (poster)

Monitoring morphometric drift in lifelong learning segmentation of the spinal cord.

Enamundram Naga Karthik

Sandrine Bédard

Jan Valosek

Christoph S. Aigner

Élise Bannier

Josef Bednařík

Virginie Callot

Anna Combes

Armin Curt

Gergely David

Falk Eippert

Lynn Farner

Michael G Fehlings

Patrick Freund

Tobias Granberg

Cristina Granziera

Rhscir Network Imaging Group

Ulrike Horn

Tomáš Horák

Suzanne Humphreys … (see 36 more)

Markus Hupp

Anne Kerbrat

Nawal Kinany

Shannon Kolind

Petr Kudlička

Anna Lebret

Lisa Eunyoung Lee

Caterina Mainero

Allan R. Martin

Megan McGrath

Govind Nair

Kristin P. O'Grady

Jiwon Oh

Russell Ouellette

Nikolai Pfender

Dario Pfyffer

Pierre-François Pradat

Alexandre Prat

Emanuele Pravatà

Daniel S. Reich

Ilaria Ricchi

Naama Rotem-Kohavi

Simon Schading-Sassenhausen

Maryam Seif

Andrew Smith

Seth A Smith

Grace Sweeney

Roger Tam

Anthony Traboulsee

Constantina Andrada Treaba

Charidimos Tsagkas

Zachary Vavasour

Dimitri Van De Ville

Kenneth Arnold Weber II

Julien Cohen-Adad

Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and … (see more)injuries affecting the spinal cord. For instance, the spinal cord cross-sectional area can be used to monitor cord atrophy in multiple sclerosis and to characterize compression in degenerative cervical myelopathy. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite (n=75) dataset, including 9 different MRI contrasts and several spinal cord pathologies. We also introduce a lifelong learning framework to automatically monitor the morphometric drift as the model is updated using additional datasets. The framework is triggered by an automatic GitHub Actions workflow every time a new model is created, recording the morphometric values derived from the model's predictions over time. As a real-world application of the proposed framework, we employed the spinal cord segmentation model to update a recently-introduced normative database of healthy participants containing commonly used measures of spinal cord morphometry. Results showed that: (i) our model performs well compared to its previous versions and existing pathology-specific models on the lumbar spinal cord, images with severe compression, and in the presence of intramedullary lesions and/or atrophy achieving an average Dice score of 0.95 ± 0.03; (ii) the automatic workflow for monitoring morphometric drift provides a quick feedback loop for developing future segmentation models; and (iii) the scaling factor required to update the database of morphometric measures is nearly constant among slices across the given vertebral levels, showing minimum drift between the current and previous versions of the model monitored by the framework. The model is freely available in Spinal Cord Toolbox v7.0.

2026-01-21

Imaging Neuroscience (published)

Protein Language Models: Is Scaling Necessary?

Quentin Fournier

Robert M. Vernon

Almer Van Der Sloot

Benjamin Schulz

Christopher James Langmead

Public protein sequence databases contain samples from the fitness landscape explored by nature. Protein language models (pLMs) pre-trained … (see more)on these sequences aim to capture this landscape for tasks like property prediction and protein design. Following the same trend as in natural language processing, pLMs have continuously been scaled up. However, the premise that scale leads to better performance assumes that source databases provide an accurate representation of the underlying fitness landscape, which is likely false. By developing an efficient codebase, designing a modern architecture, and addressing data quality concerns such as sample bias, we introduce AMPLIFY, a best-in-class pLM that is orders of magnitude less expensive to train and deploy than previous models. Furthermore, to support the scientific community and democratize the training of pLMs, we have open-sourced AMPLIFY’s pre-training codebase, data, and model checkpoints.

2026-01-04

BioRxiv (preprint)

Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning

Nilaksh

In streaming Reinforcement Learning (RL), transitions are observed and discarded immediately after a single update. While this minimizes res… (see more)ource usage for on-device applications, it makes agents notoriously sample-inefficient, since value-based losses alone struggle to extract meaningful representations from transient data. We propose extending Self-Predictive Representations (SPR) to the streaming pipeline to maximize the utility of every observed frame. However, due to the highly correlated samples induced by the streaming regime, naively applying this auxiliary loss results in training instabilities. Thus, we introduce orthogonal gradient updates relative to the momentum target and resolve gradient conflicts arising from streaming-specific optimizers. Validated across the Atari, MinAtar, and Octax suites, our approach systematically outperforms existing streaming baselines. Latent-space analysis, including t-SNE visualizations and effective-rank measurements, confirms that our method learns significantly richer representations, bridging the performance gap caused by the absence of a replay buffer, while remaining efficient enough to train on just a few CPU cores.

2025-12-31

International Conference on Machine Learning (Accept (regular))

The Expressive Limits of Diagonal SSMs for State-Tracking

State-Space Models (SSMs) have recently been shown to achieve strong empirical performance on a variety of long-range sequence modeling task… (see more)s while remaining efficient and highly-parallelizable. However, the theoretical understanding of their expressive power remains limited. In this work, we study the expressivity of input-Dependent Complex-valued Diagonal (DCD) State-Space Models (SSMs) on sequential state-tracking tasks for abstract groups. It is easy to show that a single DCD SSM layer with a universal decoder can track any Abelian group at finite precision by decomposing it into a product of cyclic groups. We show that this is tight by proving that such a model cannot track any non-Abelian group at finite precision. We further establish the expressivity of multi-layer DCD SSMs. We show that a

2025-12-31

International Conference on Learning Representations (Accept (Poster))

The Markovian Thinker

Milad Aghajohari

Kamran Chitsaz

Amirhossein Kazemnejad

Reasoning LLMs suffer from quadratic compute growth as their context length increases, making reinforcement learning with verifiable rewards… (see more) (RLVR) and test-time scaling prohibitively expensive. Prior work has tried to lighten the computational burden by shortening reasoning traces through pruning, summarization, or multi-stage training, but these methods remain bound to quadratic costs. We introduce Delethink, a thinking algorithm that realizes the Markovian Thinking Paradigm. Instead of producing one long monolithic reasoning trace, Delethink thinks in a sequence of chunks, the Delethink trace. Each chunk continues reasoning by referring only to a fixed number of prior tokens, which functions as a Markovian state sufficient for progressing reasoning, while deleting the rest. This preserves continuity without carrying the quadratic baggage. As a result, compute scales linearly and peak memory remains constant. In experiments, we show that Delethink can be applied directly to off-the-shelf reasoning models ranging from

2025-12-31

International Conference on Learning Representations (Accept (Poster))

Arjun Vaithilingam Sudhakar

A Generalist Hanabi Agent

Hadi Nekoei

Mathieu Reymond

Miao Liu

Janarthanan Rajendran

Traditional multi-agent reinforcement learning (MARL) systems can develop cooperative strategies through repeated interactions. However, the… (see more)se systems are unable to perform well on any other setting than the one they have been trained on, and struggle to successfully cooperate with unfamiliar collaborators. This is particularly visible in the Hanabi benchmark, a popular 2-to-5 player cooperative card-game which requires complex reasoning and precise assistance to other agents. Current MARL agents for Hanabi can only learn one specific game-setting (e.g., 2-player games), and play with the same algorithmic agents. This is in stark contrast to humans, who can quickly adjust their strategies to work with unfamiliar partners or situations. In this paper, we introduce Recurrent Replay Relevance Distributed DQN (R3D2), a generalist agent for Hanabi, designed to overcome these limitations. We reformulate the task using text, as language has been shown to improve transfer. We then propose a distributed MARL algorithm that copes with the resulting dynamic observation- and action-space. In doing so, our agent is the first that can play all game settings concurrently, and extend strategies learned from one setting to other ones. As a consequence, our agent also demonstrates the ability to collaborate with different algorithmic agents -- agents that are themselves unable to do so. The implementation code is available at:

2025-04-22

International Conference on Learning Representations (Accept (Poster))