Publications

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
Tian Jin
Ahmed Imtiaz Humayun
Utku Evci
Suvinay Subramanian
Amir Yazdanbakhsh
Dan Alistarh
Gintare Karolina Dziugaite
Pruning eliminates unnecessary parameters in neural networks; it offers a promising solution to the growing computational demands of large l… (voir plus)anguage models (LLMs). While many focus on post-training pruning, sparse pre-training--which combines pruning and pre-training into a single phase--provides a simpler alternative. In this work, we present the first systematic exploration of optimal sparse pre-training configurations for LLMs through an examination of 80 unique pruning schedules across different sparsity levels and training durations. We find that initiating pruning at 25% of total training compute and concluding at 75% achieves near-optimal final evaluation loss. These findings provide valuable insights for efficient and effective sparse pre-training of LLMs. Furthermore, we propose a new scaling law that modifies the Chinchilla scaling law to use the average parameter count over pre-training. Through empirical and theoretical validation, we demonstrate that this modified scaling law accurately models evaluation loss for both sparsely and densely pre-trained LLMs, unifying scaling laws across pre-training paradigms. Our findings indicate that while sparse pre-training achieves the same final model quality as dense pre-training for equivalent compute budgets, it provides substantial benefits through reduced model size, enabling significant potential computational savings during inference.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
On the Modeling Capabilities of Large Language Models for Sequential Decision Making
Martin Klissarov
R Devon Hjelm
Alexander T Toshev
Bogdan Mazoure
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
On the Transfer of Object-Centric Representation Learning.
Aniket Rajiv Didolkar
Andrii Zadaianchuk
Anirudh Goyal
Michael Curtis Mozer
Yoshua Bengio
Georg Martius
Maximilian Seitzer
2025-01-21
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
Towards General-Purpose Model-Free Reinforcement Learning
Scott Fujimoto
Pierluca D'Oro
Amy Zhang
Yuandong Tian
Michael G. Rabbat
Reinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored t… (voir plus)o specific benchmarks, relying on carefully tuned hyperparameters and algorithmic choices. Recently, powerful model-based RL methods have shown impressive general results across benchmarks but come at the cost of increased complexity and slow run times, limiting their broader applicability. In this paper, we attempt to find a unifying model-free deep RL algorithm that can address a diverse class of domains and problem settings. To achieve this, we leverage model-based representations that approximately linearize the value function, taking advantage of the denser task objectives used by model-based RL while avoiding the costs associated with planning or simulated trajectories. We evaluate our algorithm, MR.Q, on a variety of common RL benchmarks with a single set of hyperparameters and show a competitive performance against domain-specific and general baselines, providing a concrete step towards building general-purpose model-free deep RL algorithms.
2025-01-21
ICLR.cc/2025/Conference (spotlight)
doi.org
openreview.net
Towards Interpreting Visual Information Processing in Vision-Language Models
Clement Neo
Luke Ong
Philip Torr
Mor Geva
David M. Krueger
Fazl Barez
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Towards whole-genome inference of polygenic scores with fast and memory-efficient algorithms
Shadi Zabad
Chirayu Anant Haryan
Simon Gravel
Sanchit Misra
Yuemei Li
2025-01-21
bioRxiv (prépublication)
doi.org
Training Language Models to Self-Correct via Reinforcement Learning
Aviral Kumar
Vincent Zhuang
Rishabh Agarwal
Yi Su
John D Co-Reyes
Avi Singh
Kate Baumli
Shariq Iqbal
Colton Bishop
Rebecca Roelofs
Lei M Zhang
Kay McKinney
Disha Shrivastava
Cosmin Paduraru
George Tucker
Doina Precup
Feryal Behbahani
Aleksandra Faust
Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffecti… (voir plus)ve in modern LLMs. Existing approaches for training self-correction either require multiple models or rely on a more capable model or other forms of supervision. To this end, we develop a multi-turn online reinforcement learning (RL) approach, SCoRe, that significantly improves an LLM's self-correction ability using entirely self-generated data. To build SCoRe, we first show that variants of supervised fine-tuning (SFT) on offline model-generated correction traces are insufficient for instilling self-correction behavior. In particular, we observe that training via SFT either suffers from a distribution mismatch between the training data and the model's own responses or implicitly prefers only a certain mode of correction behavior that is often not effective at test time. SCoRe addresses these challenges by training under the model's own distribution of self-generated correction traces and using appropriate regularization to steer the learning process into learning a self-correction strategy that is effective at test time as opposed to simply fitting high-reward responses for a given prompt. This regularization prescribes running a first phase of RL on a base model to generate a policy initialization that is less susceptible to collapse and then using a reward bonus to amplify self-correction during training. When applied to Gemini 1.0 Pro and 1.5 Flash models, we find that SCoRe achieves state-of-the-art self-correction performance, improving the base models' self-correction by 15.6% and 9.1% respectively on the MATH and HumanEval benchmarks.
2025-01-21
ICLR.cc/2025/Conference (présentation orale)
doi.org
openreview.net
What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models
Ahmed Imtiaz Humayun
Ibtihel Amara
Candice Schumann
Golnoosh Farnadi
Negar Rostamzadeh
Mohammad Havaei
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Supervised Large Neighbourhood Search for MIPs
Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger
Large Neighbourhood Search (LNS) is a powerful heuristic framework for solving Mixed-Integer Programming (MIP) problems. However, designing … (voir plus)effective variable selection strategies in LNS remains challenging, especially for diverse sets of problems. In this paper, we propose an approach that integrates Machine Learning (ML) within the destroy operator of LNS for MIPs with a focus on minimal offline training. We implement a modular LNS matheuristic as a test bench to compare different LNS heuristics, including our ML-enhanced LNS. Experimental results on the MIPLIB 2017 dataset demonstrate that the matheuristic can significantly improve the performance of state-of-the-art solvers like Gurobi and SCIP. We conduct analyses on noisy oracles to explore the impact of prediction accuracy on solution quality. Additionally, we develop techniques to enhance the ML model through loss adjustments and sampling routines. Our findings suggest that while random LNS remains competitive, our Supervised LNS (SLNS) outperforms other baselines and helps set the foundation for future research on ML for LNS methods that are both efficient and general.
2025-01-17
ArXiv (prépublication)
arxiv.org
arxiv.org
CHIRP: A Fine-Grained Benchmark for Open-Ended Response Evaluation in Vision-Language Models
Alexis Roger
Prateek Humane
Daniel Z Kaplan
Kshitij Gupta
Qirui Sun
George Adamopoulos
Jonathan Siu Chi Lim
Quentin Gregory Anthony
Edwin Fennell
Irina Rish
The proliferation of Vision-Language Models (VLMs) in the past several years calls for rigorous and comprehensive evaluation methods and ben… (voir plus)chmarks. This work analyzes existing VLM evaluation techniques, including automated metrics, AI-based assessments, and human evaluations across diverse tasks. We first introduce Robin - a novel suite of VLMs that we built by combining Large Language Models (LLMs) and Vision Encoders (VEs) at multiple scales, and use Robin to identify shortcomings of current evaluation approaches across scales. Next, to overcome the identified limitations, we introduce CHIRP - a new long form response benchmark we developed for more robust and complete VLM evaluation. We provide open access to the Robin training code, model suite, and CHIRP benchmark to promote reproducibility and advance VLM research.
2025-01-15
ArXiv (prépublication)
doi.org
arxiv.org
Maximizing Data and Hardware Reuse for HLS with Early-Stage Symbolic Partitioning.
Tzung-Han Juang
Christophe Dubach
While traditional High-Level Synthesis (HLS) converts “high-level” C-like programs into hardware automatically, producing high-performan… (voir plus)ce designs still requires hardware expertise. Optimizations such as data partitioning can have a large impact on performance since they directly affect data reuse patterns and the ability to reuse hardware. However, optimizing partitioning is a difficult process since minor changes in the parameter choices can lead to totally unpredictable performance. Functional array-based languages have been proposed instead of C-based approaches, as they offer stronger performance guarantees. This article proposes to follow a similar approach and exposes a divide-and-conquer primitive at the algorithmic level to let users partition any arbitrary computation. The compiler is then free to explore different partition shapes to maximize both data and hardware reuse automatically. The main challenge remains that the impact of partitioning is only known much later in the compilation flow. This is due to the hard-to-predict effects of the many optimizations applied during compilation. To solve this problem, the partitioning is expressed using a set of symbolic tunable parameters, introduced early in the compilation pipeline. A symbolic performance model is then used in the last compilation stage to predict performance based on the possible values of the tunable parameters. Using this approach, a design space exploration is conducted on an Intel Arria 10 Field Programmable Gate Arrays (FPGAs), and competitive performance is achieved on the classical VGG and TinyYolo neural networks.
2025-01-15
ACM Transactions on Architecture and Code Optimization (TACO) (publié)
doi.org
High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare
Maelenn Corfmat
Joé T. Martineau
Catherine Régis
BACKGROUND: Considering the disruptive potential of AI technology, its current and future impact in healthcare, as well as healthcare profes… (voir plus)sionals' lack of training in how to use it, the paper summarizes how to approach the challenges of AI from an ethical and legal perspective. It concludes with suggestions for improvements to help healthcare professionals better navigate the AI wave. METHODS: We analyzed the literature that specifically discusses ethics and law related to the development and implementation of AI in healthcare as well as relevant normative documents that pertain to both ethical and legal issues. After such analysis, we created categories regrouping the most frequently cited and discussed ethical and legal issues. We then proposed a breakdown within such categories that emphasizes the different - yet often interconnecting - ways in which ethics and law are approached for each category of issues. Finally, we identified several key ideas for healthcare professionals and organizations to better integrate ethics and law into their practices. RESULTS: We identified six categories of issues related to AI development and implementation in healthcare: (1) privacy; (2) individual autonomy; (3) bias; (4) responsibility and liability; (5) evaluation and oversight; and (6) work, professions and the job market. While each one raises different questions depending on perspective, we propose three main legal and ethical priorities: education and training of healthcare professionals, offering support and guidance throughout the use of AI systems, and integrating the necessary ethical and legal reflection at the heart of the AI tools themselves. CONCLUSIONS: By highlighting the main ethical and legal issues involved in the development and implementation of AI technologies in healthcare, we illustrate their profound effects on professionals as well as their relationship with patients and other organizations in the healthcare sector. We must be able to identify AI technologies in medical practices and distinguish them by their nature so we can better react and respond to them. Healthcare professionals need to work closely with ethicists and lawyers involved in the healthcare system, or the development of reliable and trusted AI will be jeopardized.
2025-01-14
BMC Medical Ethics (publié)
doi.org