Gauthier Gidel

sadhana.anand@mila.quebec

Biography

I am an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, a core academic member of Mila – Quebec Artificial Intelligence Institute, and a Canada CIFAR AI Chair.

Previously, I was awarded a Borealis AI Graduate Fellowship, worked at DeepMind and Element AI, and was a Long-Term Visitor at the Simons Institute at UC Berkeley.

My research interests lie at the intersection of game theory, optimization and machine learning.

Current Students

Sadhana Anand

Master's Research - Université de Montréal

yuan-chen.chang@mila.quebec

Yuan-Chen Chang

Research Intern - Université de Montréal

David Dobre

PhD - Université de Montréal

david-a.dobre@mila.quebec

damien.ferbach@mila.quebec

Nouha Dziri

Independent visiting researcher - N/A

nouha.dziri@gmail.com

PhD - Université de Montréal

Co-supervisor :

Courtney Paquette

niklas.herbster@gmail.com

Niklas Herbster

Research Intern - Université de Montréal

Principal supervisor :

Irina Rish

marco.jiralerspong@mila.quebec

Marco Jiralerspong

PhD - Université de Montréal

Zichu Liu

PhD - Université de Montréal

Co-supervisor :

Ioannis Mitliagkas

zichu.liu@mila.quebec

Orel Mazor

Collaborating researcher - ENS Lyon

orel.mazor@mila.quebec

andjela.mladenovic@mila.quebec

Andjela Mladenovic

PhD - Université de Montréal

Co-supervisor :

Aaron Courville

mehrnaz.mofakhami@mila.quebec

Mehrnaz Mofakhami

Collaborating researcher - Université de Montréal

Co-supervisor :

Ioannis Mitliagkas

Collaborating researcher - Université de Montréal

ailin.rasteh@mila.quebec

Independent visiting researcher - Technical Univeristy of Munich

leo.schwinn@mila.quebec

Tom Stanic

Research Intern - Université de Montréal

tom.stanic@mila.quebec

tommaso.tosato@mila.quebec

Tommaso Tosato

Postdoctorate - Université de Montréal

PhD - Université de Montréal

danilo.vucetic@mila.quebec

bora.yongacoglu@mila.quebec

Sophie Xhonneux

PhD - Université de Montréal

Co-supervisor :

Collaborating Alumni - N/A

Blog Posts

June 13, 2024

What Do Synaptic Weight Distributions Tell Us About Learning in the Brain ?

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Read the article

Publications

Accelerated and Stable Convergence with Anchored Optimistic Method

Jianxin You

We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the… (see more) extragradient method rely on two gradient queries per iteration, which limits their analysis and applicability in the online and stochastic settings. We propose a family of Generalized Optimistic Methods with Anchoring (GOMA), which combine two-time-scale optimistic updates with an anchoring term inspired by Halpern iteration. In the deterministic setting, GOMA achieves the optimal accelerated last-iterate rate

2026-06-18

arXiv (preprint)

Why Open Source? A Game-Theoretic Analysis of the AI Race

Andjela Mladenovic

Aaron Courville

In recent years, with the advancement of frontier AI, we have observed certain dynamics in open-sourcing and closed-sourcing decisions. We p… (see more)ropose a game-theoretic model to analyze these dynamics in the current landscape of the AI race. Our model builds on an R&D race framework under a winner-takes-all setting, and it accounts for the cases where the players' actions can be either discrete or continuous (i.e., partial open-sourcing, such as open weights). We show that determining the existence of a discrete pure non-trivial Nash equilibrium is NP-hard in general but that we can transform the discrete Nash existence computation into a MIP (Mixed-Integer Programming) problem, making it tractable for small instances using a standard MIP solver. Next, we show the existence and tractability of pure Nash equilibria in the continuous version of our problem, leveraging standard convex analysis results, and constructing an equivalent MIP formulation. Throughout this work, we leverage both our main technical results as well as surrounding technical analysis, to derive socially relevant insights that we believe can serve both to understand already existing decisions and dynamics and to potentially inform new policies.

2026-04-16

arXiv (preprint)

Soft Mellowmax Monte Carlo Planning

Danilo Vucetic

Soft mellowmax (SMM) recently emerged as an alternative operator in Q-learning, achieving impressive performance in games and scientific dis… (see more)covery tasks. Despite SMM's ability to achieve high returns and its enticing robustness, diversity, and sample efficiency characteristics, SMM has not yet been translated into a Monte Carlo tree search algorithm. To address this gap, a soft mellowmax-based Monte Carlo tree search algorithm, SMM-TS, is proposed and theoretically justified. It is empirically demonstrated that SMM-TS converges significantly faster than other tree search methods in synthetic environments, while maintaining competitive performance in games. The fast convergence of SMM-TS makes recursive self-improvement loops more scalable, while the stability gained via planning and the robustness of the operator make SMM-TS more practical for agents operating in uncertain and changing environments.

2026-03-04

RSI @ International Conference on Learning Representations (poster)

Beyond Reward Maximization: Evaluating the Diversity of Trajectories in Reinforcement Learning with Temporal Vendi Score

In domains such as scientific discovery and automated design using reinforcement learning (RL), the final task of an agent should extend bey… (see more)ond maximising a single scalar reward; it requires identifying diverse sets of high-quality trajectories to uncover distinct solutions that can provide novel insights on how to solve the problems of interest and transfer robustly from simulation to the real world. However, the RL literature currently lacks a holistic, domain-agnostic standard for measuring trajectory diversity. Existing metrics have been developed to improve exploration at training time but not to evaluate and compare diversity induced by different agents, rendering cross-method comparisons inconsistent and challenging. To address this, we introduce the Temporal Vendi Score (TVS), a novel metric designed to evaluate the diversity of an RL agent by computing the entropy of the eigenvalues' similarity matrix of sampled trajectories. Unlike previous approaches, our metric captures the behavioural diversity of trajectories by accounting for both the sequential nature of state visitations and the temporal structure of the underlying MDP, rather than relying on order-agnostic state comparisons. We validate the TVS on simple environments where we can control the number of different ways a problem can be solved, demonstrating that it provides a more robust, semantically meaningful ranking of diversity than standard baselines. We then show that our metric can scale to a high-dimensional, continuous environment.

2026-03-01

LLA @ International Conference on Learning Representations (poster)

Logarithmic-time Schedules for Scaling Language Models with Momentum

In practice, the hyperparameters …

2026-02-04

arXiv (preprint)

Accelerated and Stable Convergence with Anchored Generalized Optimistic Method

Jianxin You

We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the… (see more) extragradient method rely on two gradient queries per iteration, which limits their analysis and applicability in the online and stochastic settings. We propose a family of Generalized Optimistic Methods with Anchoring (GOMA), which combine two time-scale optimistic updates with an anchoring term inspired by Halpern iteration. In particular, we show that for monotone Lipschitz operators, GOMA achieves an accelerated last-iterate convergence rate of

2025-12-31

International Conference on Machine Learning (Accept (regular))

A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness

Moritz Ladenburger

Tim Beyer

Mehrnaz Mofakhami

Stephan Günnemann

Automated \enquote{LLM-as-a-Judge} frameworks have become the de facto standard for scalable evaluation across natural language processing. … (see more)For instance, in safety evaluation, these judges are relied upon to evaluate harmfulness in order to benchmark the robustness of safety against adversarial attacks. However, we show that existing validation protocols fail to account for substantial distribution shifts inherent to red-teaming: diverse victim models exhibit distinct generation styles, attacks distort output patterns, and semantic ambiguity varies significantly across jailbreak scenarios. Through a comprehensive audit using 6642 human-verified labels, we reveal that the unpredictable interaction of these shifts often causes judge performance to degrade to near random chance. This stands in stark contrast to the high human agreement reported in prior work. Crucially, we find that many attacks inflate their success rates by exploiting judge insufficiencies rather than eliciting genuinely harmful content. To enable more reliable evaluation, we propose ReliableBench, a benchmark of behaviors that remain more consistently judgeable, and JudgeStressTest, a dataset designed to expose judge failures. (Data in supplement).

2025-12-31

International Conference on Machine Learning (Accept (regular))

Position: LLM-Safety Evaluations Lack Robustness

Tim Beyer

Sophie Xhonneux

Simon Geisler

Stephan Günnemann

In this position paper, we argue that current safety alignment research efforts for large language models are hindered by many intertwined s… (see more)ources of noise, such as small datasets, methodological inconsistencies, and unreliable evaluation setups. This can, at times, make it impossible to evaluate and compare attacks and defenses fairly, thereby slowing research progress. We systematically analyze the LLM safety evaluation pipeline, covering dataset curation, optimization strategies for automated red-teaming, response generation, and response evaluation using LLM judges. At each stage, we identify key issues and highlight their practical impact. We also propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers. Lastly, we offer an opposing perspective, highlighting practical reasons for existing limitations. We believe that addressing the outlined problems in future research will improve the field’s ability to generate easily comparable results and make measurable progress.

2025-12-31

International Conference on Machine Learning (Accept (regular))

Dimension-adapted Momentum Outscales SGD

We investigate scaling laws for stochastic momentum algorithms with small batch on the power law random features model, parameterized by dat… (see more)a complexity, target complexity, and model size. When trained with a stochastic momentum algorithm, our analysis reveals four distinct loss curve shapes determined by varying data-target complexities. While traditional stochastic gradient descent with momentum (SGD-M) yields identical scaling law exponents to SGD, dimension-adapted Nesterov acceleration (DANA) improves these exponents by scaling momentum hyperparameters based on model size and data complexity. This outscaling phenomenon, which also improves compute-optimal scaling behavior, is achieved by DANA across a broad range of data and target complexities, while traditional methods fall short. Extensive experiments on high-dimensional synthetic quadratics validate our theoretical predictions and large-scale text experiments with LSTMs show DANA's improved loss exponents over SGD hold in a practical setting.

2025-09-17

NeurIPS.cc/2025/Conference (spotlight)

Tight Lower Bounds and Improved Convergence in Performative Prediction

Performative prediction is a framework accounting for the shift in the data distribution induced by the prediction of a model deployed in th… (see more)e real world. Ensuring rapid convergence to a stable solution where the data distribution remains the same after the model deployment is crucial, especially in evolving environments. This paper extends the Repeated Risk Minimization (RRM) framework by utilizing historical datasets from previous retraining snapshots, yielding a class of algorithms that we call Affine Risk Minimizers and enabling convergence to a performatively stable point for a broader class of problems. We introduce a new upper bound for methods that use only the final iteration of the dataset and prove for the first time the tightness of both this new bound and the previous existing bounds within the same regime. We also prove that utilizing historical datasets can surpass the lower bound for last iterate RRM, and empirically observe faster convergence to the stable point on various performative prediction benchmarks. We offer at the same time the first lower bound analysis for RRM within the class of Affine Risk Minimizers, quantifying the potential improvements in convergence speed that could be achieved with other variants in our framework.

2025-09-17

Conference on Neural Information Processing Systems (poster)

Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning

Marco Jiralerspong

Esther Derman

Danilo Vucetic

Nikolay Malkin

Bilun Sun

Tianyu Zhang

Pierre-Luc Bacon

A major bottleneck in scientific discovery consists of narrowing an exponentially large set of objects, such as proteins or molecules, to a … (see more)small set of promising candidates with desirable properties. While this process can rely on expert knowledge, recent methods leverage reinforcement learning (RL) guided by a proxy reward function to enable this filtering. By employing various forms of entropy regularization, these methods aim to learn samplers that generate diverse candidates that are highly rated by the proxy function. In this work, we make two main contributions. First, we show that these methods are liable to generate overly diverse, suboptimal candidates in large search spaces. To address this issue, we introduce a novel unified operator that combines several regularized RL operators into a general framework that better targets peakier sampling distributions. Secondly, we offer a novel, robust RL perspective of this filtering process. The regularization can be interpreted as robustness to a compositional form of uncertainty in the proxy function (i.e., the true evaluation of a candidate differs from the proxy's evaluation). Our analysis leads us to a novel, easy-to-use algorithm we name trajectory general mellowmax (TGM): we show it identifies higher quality, diverse candidates than baselines in both synthetic and real-world tasks. Code: https://github.com/marcojira/tgm.

2025-06-19

ArXiv (preprint)

Jailbreak Distillation: Renewable Safety Benchmarking

Jingyu Zhang

Ahmed Elgohary

Xiawei Wang

A S M Iftekhar

Ahmed Magooda

Benjamin Van Durme

Daniel Khashabi

Kyle Jackson

JBDistill Benchmark JBDistill Benchmark

Marah Ihab Abdin

Jyoti Aneja

Harkirat Singh Behl

Sébastien Bubeck

Ronen Eldan

S. Gunasekar

Michael Harrison

Russell J. Hewett

Mojan Javaheripi

Piero Kauffmann

James R. Lee … (see 484 more)

Yin Tat Lee

Yuanzhi Li

Weishung Liu

C. C. T. Mendes

Anh Nguyen

Eric Price

Gustavo de Rosa

Olli Saarikivi

Adil Salim

Tim Beyer

Sophie Xhonneux

Simon Geisler

Stephan Günnemann. 2025

Blake Bullwinkel

Amanda Minnich

Shiven Chawla

Gary Lopez

Martin Pouliot

Whitney Maxwell

Patrick Chao

Edoardo Debenedetti

Alexander Robey

Maksym Andriushchenko

Francesco Croce

Vikash Sehwag

Edgar Dobriban

Nicolas Flammarion

George J. Pappas

Florian Tramèr

Hamed Hassani

Eric Wong

Jailbreakbench

Zora Che

Stephen Casper

Robert Kirk

Anirudh Satheesh

Stewart Slocum

Lev E McKinney

Rohit Gandikota

Aidan Ewart

Domenic Rosati

Zichu Wu

Zikui Cai

Daya Guo

Dejian Yang

Haowei Zhang

Jun-Mei Song

Ruoyu Zhang

Runxin Xu

Qihao Zhu

Shirong Ma

Peiyi Wang

Xiaoling Bi

Xiaokang Zhang

Xingkai Yu

Yu Wu

Z. F. Wu

Zhibin Gou

Zhihong Shao

Zhuoshu Li

Ziyi Gao

A. Liu

Bing Xue

Bingxuan Wang

Bo WU

Bei Feng

Chenggang Lu

Chenggang Zhao

Chengqi Deng

Chenyu Zhang

C. Ruan

Damai Dai

Deli Chen

Dong-Li Ji

Erhang Li

Fangyun Lin

Fucong Dai

Fuli Luo

Guangbo Hao

Guanting Chen

Guowei Li

Han Bao

Hanwei Xu

Haocheng Wang

Honghui Ding

Huajian Xin

Huazuo Gao

Hui Qu

Hui Li

Jianzhong Guo

Jiashi Li

Jiawei Wang

Jingchang Chen

Jingyang Yuan

Junjie Qiu

Junlong Li

Jinbo Cai

Jia Ni

Jian Liang

Jin Chen

Kai Dong

Kai Hu

Kaige Gao

Kang Guan

Kexin Huang

Kuai Yu

Lean Wang

Lecong Zhang

Liang Zhao

Litong Wang

Liyue Zhang

Lei Xu

Leyi Xia

Mingchuan Zhang

Minghua Zhang

Min Tang

Meng Li

Miaojun Wang

Mingming Li

Ning Tian

Panpan Huang

Meng Wang

Qiancheng Wang

Qinyu Chen

Qiushi Du

Ruiqi Ge

Ruisong Zhang

Ruizhe Pan

Runji Wang

R. J. Chen

Rong Jin

Ruyi Chen

Shanghao Lu

Shangyan Zhou

Shanhuang Chen

Shengfeng Ye

Shiyu Wang

Shuiping Yu

Shunfeng Zhou

Shuting Pan

S. S. Li

Shuang Zhou

Shao-Ping Wu

Tao Yun

Tian Pei

Tianyu Sun

T. Wang

Wangding Zeng

Wanjia Zhao

Wen Liu

Wenfeng Liang

Wenjun Gao

Wen-Xuan Yu

Wentao Zhang

Wei Xiao

Wei An

Xiaodong Liu

Xiaohan Wang

Xiaokang Chen

Xiaotao Nie

Xin Cheng

Jian Li

Xinfeng Xie

Xingchao Liu

Xinyu Yang

Xinyuan Li

Xuecheng Su

Xuheng Lin

Xiangyu Jin

Xi-Cheng Shen

Xiaosha Chen

Xiaowen Sun

Xiaoxi-ang Wang

Xinnan Song

Xinyi Zhou

Xianzu Wang

Xinxia Shan

Y. K. Li

Y. Q. Wang

Y. X. Wei

Yang Zhang

Yan-Hong Xu

Yao Zhao

Yaofeng Sun

Yaohui Wang

Yi Yu

Yichao Zhang

Yifan Shi

Yi Xiong

Ying He

Yishi Piao

Yisong Wang

Yi Chern Tan

Yiyang Ma

Yiyuan Liu

Yongqiang Guo

Yuan Ou

Yuduan Wang

Yue Gong

Yuheng Zou

Yuzi He

Yunfan Xiong

Yuxiang Luo

Yuxiang You

Yu-mei You

Yuxuan Liu

Yuyang Zhou

Y. X. Zhu

Yanping Huang

Yaohui Li

Yang Li

Yi Zheng

Yunxiang Ma

Ying Tang

Yukun Zha

Yuting Yan

Z. Z. Ren

Zehui Ren

Zhangli Sha

Zhe Fu

Zhean Xu

Zhenda Xie

Zhengyan Zhang

Zhewen Hao

Zhicheng Ma

Zhigang Yan

Zhiyu Wu

Zihui Gu

Zijia Zhu

Zijun Liu

Zi-An Li

Ziwei Xie

Ziyang Song

Deep Ganguli

Liane Lovitt

Jackson Kernion

Amanda Askell

Yuntao Bai

Saurav Kadavath

Benjamin Mann

Ethan Perez

Nicholas Schiefer

Kamal Ndousse

Andy Jones

Sam Bowman

Anna Chen

Tom Con-erly

Nova Dassarma

Dawn Drain

Nelson Elhage Sheer

Stanislav Fort

Zac Hatfield-Dodds

T. Henighan

Danny Hernandez

Tristan Hume

Josh Jacobson

Scott Johnston

Shauna Kravec

Catherine Olsson

Sam Ringer

Eli Tran-Johnson

Dario Amodei

Tom Brown

Nicholas Joseph

Sam McCandlish

Chris Olah

Jared Kaplan

Jack Clark. 2022. Red

Aaron Grattafiori

Abhimanyu Dubey

Abhinav Jauhri

Abhinav Pandey

Abhishek Kadian

Ahmad Al-Dahle

Aiesha Letman

Akhil Mathur

Alan Schel-ten

Alex Vaughan

Amy Yang

Angela Fan

Anirudh Goyal

A. Hartshorn

Aobo Yang

Archi Mitra

Archie Sravankumar

Artem Korenev

Arthur Hinsvark

Arun Rao

Aston Zhang

Aurelien Ro-driguez

Austen Gregerson

Ava Spataru

Baptiste Rozière

Bethany Biron

Binh Tang

Bobbie Chern

Charlotte Caucheteux

Chaya Nayak

Chloe Bi

Chris Marra

Chris McConnell

Christian Keller

Christophe Touret

Chunyang Wu

Corinne Wong

Cris-tian Cantón Ferrer

Cyrus Nikolaidis

Damien Al-lonsius

Daniel Song

Danielle Pintz

Danny Livshits

Danny Wyatt

David Esiobu

Dhruv Choudhary

Dhruv Mahajan 0001

Diego Garcia-Olano

Diego Perino

Dieuwke Hupkes

Egor Lakomkin

Ehab A. AlBadawy

Elina Lobanova

Emily Dinan

Eric Michael Smith

Filip Radenovic

Francisco Guzmán

Frank Zhang

Gabriele Synnaeve

Gabrielle Lee

Georgia Lewis

G. Thattai

Graeme Nail

Gregoire Mi-alon

Guan Pang

Guillem Cucurell

Hailey Nguyen

Han-nah Korevaar

Hu Xu

Hugo Touvron

Imanol Iliyan Zarov

Arrieta Ibarra

Is-abel Kloumann

Ishan Misra

Ivan Evtimov

Jack Zhang

Jade Copet

Jaewon Lee

Jan Geffert

Jana Vranes

Jason Park

Jay Mahadeokar

Jeet Shah

Jelmer van der Linde

Jennifer Billock

Jenny Hong

Jenya Lee

Jeremy Fu

J. Fu

Jianfeng Chi

Jianyu Huang

Jiawen Liu

Jie Wang

Jiecao Yu

Joanna Bitton

Joe Spisak

Jongsoo Park

Joseph Rocca

J. Johnstun

Joshua Saxe

Junteng Jia

Kalyan Vasuden Alwala

Karthik Prasad

Kartikeya Upasani

Kate Plawiak

Keqian Li

Kenneth Heafield

Kevin R. Stone

Khalid El-Arini

Krithika Iyer

Kshitiz Malik

Kuen-ley Chiu

Kunal Bhalla

Kushal Lakhotia

Lauren Rantala-Yeary

Laurens van der Maaten

Lawrence Chen

Liang Tan

Liz Jenkins

Louis Martin

Lovish Madaan

Lubo Malo

Lukas Blecher

Lukas Landzaat

Luke de Oliveira

Madeline Muzzi

Mahesh Pasupuleti

Mannat Singh

Manohar Paluri

Marcin Kardas

Maria Tsimpoukelli

Mathew Oldham

Mathieu Rita

Maya Pavlova

Melanie Kam-badur

Mike Lewis

Mitesh Min Si

Kumar Singh

Mona Hassan

Naman Goyal

Narjes Torabi

Niko-lay Bashlykov

Nikolay Bogoychev

Niladri S. Chatterji

Ning Zhang

Olivier Duchenne

Onur Çelebi

Patrick Alrassy

Petar Pengwei Li

Peter Weng

Prajjwal Bhargava

Pratik Dubal

Punit Praveen Krishnan

Singh Koura

Puxin Xu

Qing He

Qingxiao Dong

Ragavan Srinivasan

Raj Ganapathy

Ramon Calderer

Ricardo Silveira Cabral

Robert Stojnic

Roberta Raileanu

Rohan Maheswari

Rohit Girdhar

Rohit Patel

Ro-main Sauvestre

Ron-nie Polidoro

Roshan Sumbaly

Ross Taylor

Ruan Silva

Rui Hou

Rui Wang

S. Hosseini

Sa-hana Chennabasappa

Sanjay Singh

Sean Bell

Seo-hyun Sonia Kim

Sergey Edunov

Shaoliang Nie

Sharan Narang

Sharath Chandra Raparthy

Sheng Shen

Shengye Wan

Shruti Bhosale

Shun Zhang

Simon Van-denhende

Soumya Batra

Spencer Whitman

Sten Sootla

Stephane Collot

Suchin Gururangan

S. Borodinsky

Tamar Herman

Tara Fowler

Tarek Sheasha

Thomas Georgiou

Thomas Scialom

Tobias Speckbacher

Todor Mihaylov

Tong Xiao

Ujjwal Karn

Vedanuj Goswami

Vibhor Gupta

Vignesh Ramanathan

Viktor Kerkez

Vincent Gonguet

Vir-ginie Do

Vish Vogeti

Vitor Albiero

Vladan Petro-vic

Weiwei Chu

Wenhan Xiong

Wenyin Fu

2025-05-27

ArXiv (preprint)