Publications

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reyhane Askari-Hemmat

Mohammad Pezeshki

Elvis Dohmatob

Florian Bordes

Pietro Astolfi

Melissa Hall

Jakob Verbeek

Michal Drozdzal

Adriana Romero-Soriano

Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a nov… (see more)el framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling, enabling models to focus on the most informative synthetic samples. Rather than generating a large dataset and pruning it afterward, DP efficiently approximates the direct generation of informative samples. We theoretically show how training on challenging, informative examples improves scaling laws and empirically validate that DP achieves better scaling performance with significantly fewer training samples and iterations. On ImageNet-100, DP generates 3.4x fewer samples and requires six times fewer iterations, while on ImageNet-1k, it generates 8x fewer samples with a 30 percent reduction in iterations, all while achieving superior performance compared to prior work.

2025-04-30

ICML.cc/2025/Conference (oral)

Latent Veracity Inference for Identifying Errors in Stepwise Reasoning

Minsu Kim

Jean-Pierre R. Falet

Oliver E. Richardson

Xiaoyin Chen

Moksh J. Jain

Sungjin Ahn

Sungsoo Ahn

Yoshua Bengio

Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can cont… (see more)ain inaccurate statements that reduce performance and trustworthiness. To address this, we propose to augment each reasoning step in a CoT with a latent veracity (or correctness) variable. To efficiently explore this expanded space, we introduce Veracity Search (VS), a discrete search algorithm over veracity assignments. It performs otherwise intractable inference in the posterior distribution over latent veracity values by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time verification method facilitates supervised fine-tuning of an Amortized Veracity Inference (AVI) machine by providing pseudo-labels for veracity. AVI generalizes VS, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that VS reliably identifies errors in logical (ProntoQA), mathematical (GSM8K), and commonsense (CommonsenseQA) reasoning benchmarks, with AVI achieving comparable zero-shot accuracy. Finally, we demonstrate the utility of latent veracity inference for providing feedback during self-correction and self-improvement.

2025-04-30

arXiv (published)

Learning Penalty for Optimal Partitioning via Automatic Feature Extraction

Tung L. Nguyen

Toby Dylan Hocking

Changepoint detection identifies significant shifts in data sequences, making it important in areas like finance, genetics, and healthcare. … (see more)The Optimal Partitioning algorithms efficiently detect these changes, using a penalty parameter to limit the changepoints number. Determining the appropriate value for this penalty can be challenging. Traditionally, this process involved manually extracting statistical features, such as sequence length or variance to make the prediction. This study proposes a novel approach that uses recurrent neural networks to learn this penalty directly from raw sequences by automatically extracting features. Experiments conducted on 20 benchmark genomic datasets show that this novel method surpasses traditional methods in partitioning accuracy in most cases.

2025-04-30

arXiv (published)

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D

Sergio Arnaud

Paul McVay

Ada Martin

Arjun Majumdar

Krishna Murthy

Phillip Thomas

Ruslan Partsey

Daniel Dugas

Abha Gejji

Alexander Sax

Vincent-Pierre Berges

Mikael Henaff

Ayush Jain

Ang Cao

Ishita Prasad

Mrinal Kalakrishnan

Michael G. Rabbat

Nicolas Ballas

Mahmoud Assran

Oleksandr Maksymets … (see 2 more)

Aravind Rajeswaran

Franziska Meier

2025-04-30

ICML.cc/2025/Conference (poster)

Gintare Karolina Dziugaite

Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization

Phillip Huang Guo

Aaquib Syed

Abhay Sheshadri

Aidan Ewart

2025-04-30

ICML.cc/2025/Conference (poster)

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn

Hongyao Tang

Johan Obando-Ceron

Pablo Samuel Castro

Aaron Courville

Glen Berseth

Plasticity, or the ability of an agent to adapt to new tasks, environments, or distributions, is crucial for continual learning. In this pap… (see more)er, we study the loss of plasticity in deep continual RL from the lens of churn: network output variability for out-of-batch data induced by mini-batch training. We demonstrate that (1) the loss of plasticity is accompanied by the exacerbation of churn due to the gradual rank decrease of the Neural Tangent Kernel (NTK) matrix; (2) reducing churn helps prevent rank collapse and adjusts the step size of regular RL gradients adaptively. Moreover, we introduce Continual Churn Approximated Reduction (C-CHAIN) and demonstrate it improves learning performance and outperforms baselines in a diverse range of continual learning environments on OpenAI Gym Control, ProcGen, DeepMind Control Suite, and MinAtar benchmarks.

2025-04-30

International Conference on Machine Learning (poster)

Multi-Modal Language Models as Text-to-Image Model Evaluators

Jiahui Chen

Candace Ross

Reyhane Askari Hemmat

Koustuv Sinha

Melissa Hall

Michal Drozdzal

Adriana Romero

2025-04-30

arXiv (published)

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning

Guozheng Ma

Lu Li

Zilin Wang

Li Shen

Pierre-Luc Bacon

Dacheng Tao

Effectively scaling up deep reinforcement learning models has proven notoriously difficult due to network pathologies during training, motiv… (see more)ating various targeted interventions such as periodic reset and architectural advances such as layer normalization. Instead of pursuing more complex modifications, we show that introducing static network sparsity alone can unlock further scaling potential beyond their dense counterparts with state-of-the-art architectures. This is achieved through simple one-shot random pruning, where a predetermined percentage of network weights are randomly removed once before training. Our analysis reveals that, in contrast to naively scaling up dense DRL networks, such sparse networks achieve both higher parameter efficiency for network expressivity and stronger resistance to optimization challenges like plasticity loss and gradient interference. We further extend our evaluation to visual and streaming RL scenarios, demonstrating the consistent benefits of network sparsity.

2025-04-30

ICML.cc/2025/Conference (oral)

Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models

Any well-behaved generative model over a variable …

2025-04-30

International Conference on Machine Learning (poster)

Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind

Mouad Abrini

Omri Abend

Dina M. Acklin

Henny Admoni

Gregor Aichinger

Nitay Alon

Zahra Ashktorab

Ashish Atreja

Moises Auron

Alexander Aufreiter

Raghav Awasthi

Soumya Banerjee

Joseph Barnby

Rhea Basappa

Severin Bergsmann

Djallel Bouneffouf

Patrick Callaghan

Marc Cavazza

Thierry Chaminade

Sonia Chernova … (see 88 more)

Mohamed Chetouan

Moumita Choudhury

Axel Cleeremans

J. Cywinski

Fabio Cuzzolin

Hokin Deng

N'yoma Diamond

C. D. Pasquasio

Guillaume Dumas

Max J. van Duijn

Mahapatra Dwarikanath

Qingying Gao

Ashok Goel

Rebecca R. Goldstein

Matthew C. Gombolay

Gabriel Enrique Gonzalez

Amar Halilovic

Tobias Halmdienst

Mahimul Islam

Julian Jara-Ettinger

Natalie Kastel

Renana Keydar

Ashish K. Khanna

Mahdi Khoramshahi

Jihyun Kim

Mihyeon Kim

Youngbin Kim

Senka Krivic

Nikita Krasnytskyi

Arun Kumar

Junehyoung Kwon

EunJu Lee

Shane Lee

Peter R. Lewis 0001

Xue Li

Yijiang Li

Michal Lewandowski

Nathan Lloyd

Matthew B. Luebbers

Dezhi Luo

Haiyun Lyu

Dwarikanath Mahapatra

Kamal Maheshwari

Mallika Mainali

P. Mathur

Patrick Mederitsch

Shuwa Miura

Manuel Preston de Miranda

Reuth Mirsky

Shreya Mishra

Nina M. Moorman

Katelyn Morrison

John Muchovej

Bernhard Nessler

Felix Nessler

Hieu Minh Jord Nguyen

Abby Ortego

F. Papay

Antoine Pasquali

Hamed Rahimi

C. Raghu

Amanda L. Royka

Stefan Sarkadi

Jaelle Scheuerman

Simon Schmid

Paul Schrater

Anik Sen

Zahra Sheikhbahaee

Ke Shi

Reid G. Simmons

Nishant Singh

Mason O. Smith

Ramira van der Meulen

Anthia Solaki

Haoran Sun

Viktor Szolga

Matthew E. Taylor

Travis Taylor

Sanne van Waveren

Juan David Vargas

R. Verbrugge

Eitan Wagner

Justin D. Weisz

Ximing Wen

William Yeoh

Wenlong Zhang

Michelle Zhao

Shlomo Zilberstein

2025-04-30

arXiv (published)

Replication of a GWAS signal near HLA-DQA2 with AML using a disease-only cohort and external population-based controls

Rose Laflamme

Véronique Lisi

Josée Hébert

Guy Sauvageau

Sébastien Lemieux

Vincent-Philippe Lavallee

Guillaume Lettre

2025-04-30

Blood Neoplasia (published)