Megh Thakkar

How to Train Your LLM Web Agent: A Statistical Diagnosis

Dheeraj Vattikonda

Santhoshi Ravichandran

Emiliano Penaloza

Hadi Nekoei

Nicolas Gontier

Miguel Muñoz-Mármol

Sahar Omidi Shayegan

Stefania Raimondo

Xue Liu

Alexandre Piché

Alexandre Lacoste

Massimo Caccia

LLM-based web agents have recently made significant progress, but much of it has occurred in closed-source systems, widening the gap with op… (voir plus)en-source alternatives. Progress has been held back by two key challenges: first, a narrow focus on single-step tasks that overlooks the complexity of multi-step web interactions; and second, the high compute costs required to post-train LLM-based web agents. To address this, we present the first statistically grounded study on compute allocation for LLM web-agent post-training. Our approach uses a two-stage pipeline, training a Llama 3.1 8B student to imitate a Llama 3.3 70B teacher via supervised fine-tuning (SFT), followed by on-policy reinforcement learning. We find this process highly sensitive to hyperparameter choices, making exhaustive sweeps impractical. To spare others from expensive trial-and-error, we sample 1,370 configurations and use bootstrapping to estimate effective hyperparameters. Our results show that combining SFT with on-policy RL consistently outperforms either approach alone on both WorkArena and MiniWob++. Further, this strategy requires only 55% of the compute to match the peak performance of pure SFT on MiniWob++, effectively pushing the compute-performance Pareto frontier, and is the only strategy that can close the gap with closed-source models.

2025-09-17

NeurIPS.cc/2025/Conference (poster)

BIGCHARTS-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning

Ahmed Masry

Abhay Puri

Masoud Hashemi

Juan A. Rodriguez

Khyati Mahajan

Vikas Yadav

Sathwik Tejaswi Madhusudhan

Christopher Pal

Enamul Hoque

Sai Rajeswar

Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although … (voir plus)current vision-language models (VLMs) have made significant progress, they continue to struggle with chart comprehension due to training on datasets that lack diversity and real-world authenticity, or on automatically extracted underlying data tables of charts, which can contain numerous estimation errors. Furthermore, existing models only rely on supervised fine-tuning using these low-quality datasets, severely limiting their effectiveness. To address these issues, we first propose BigCharts, a dataset creation pipeline that generates visually diverse chart images by conditioning the rendering process on real-world charts sourced from multiple online platforms. Unlike purely synthetic datasets, BigCharts incorporates real-world data, ensuring authenticity and visual diversity, while still retaining accurate underlying data due to our proposed replotting process. Additionally, we introduce a comprehensive training framework that integrates supervised fine-tuning with Group Relative Policy Optimization (GRPO)-based reinforcement learning. By introducing novel reward signals specifically designed for chart reasoning, our approach enhances model robustness and generalization across diverse chart styles and domains, resulting in a state-of-the-art chart reasoning model, BigCharts-R1. Extensive experiments demonstrate that our models surpass existing methods on multiple chart question-answering benchmarks compared to even larger open-source and closed-source models.

2025-07-06

colmweb.org/COLM/2025/Conference (accepté)

Combining Domain and Alignment Vectors Provides Better Knowledge-Safety Trade-offs in LLMs

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Amal Zouaq

Payel Das

A. Chandar

2024-12-31

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (publié)

The BrowserGym Ecosystem for Web Agent Research

Maxime Gasse

Alexandre Lacoste

Massimo Caccia

Lawrence Keunho Jang

Ori Yoran

Dehan Kong

Frank F. Xu

Siva Reddy

Quentin Cappart

Graham Neubig

Ruslan Salakhutdinov

Nicolas Chapados

The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (voir plus)utomation and Large Language Models (LLMs). Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. In an earlier work, Drouin et al. (2024) introduced BrowserGym which aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature and includes AgentLab, a complementary framework that aids in agent creation, testing, and analysis. Our proposed ecosystem offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks made available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.

2024-12-31

Trans. Mach. Learn. Res. (publié)

Too Big to Fool: Resisting Deception in Language Models

Mohammad Reza Samsami

Mats Leon Richter

Juan A. Rodriguez

A. Chandar

Maxime Gasse

Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. T… (voir plus)his paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models' ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge.

2024-12-12

ArXiv (prépublication)

Fine-Tuning Web Agents: It Works, But It's Trickier Than You Think

Massimo Caccia

Léo Boisvert

Alexandre Piché

Nicolas Chapados

Maxime Gasse

Alexandre Lacoste

Recent advancements in large language models (LLMs) have sparked interest in developing autonomous web agents capable of performing digital … (voir plus)tasks through web interfaces in a human-like manner. However, even the strongest closed-source models often struggle to achieve robust results on several benchmarks, while a notable performance gap exists between them and open-source counterparts. This study investigates the potential of fine-tuning to enhance the performance of a smaller, lower-performing but cost-efficient LLM by leveraging successful traces from stronger LLMs, referred to as experts. We outline a comprehensive pipeline for data collection, filtering, and supervised fine-tuning and explore various behavior cloning parameters. Our experiments provide key insights into the challenges of fine-tuning LLMs into web agents on benchmarks like MiniWoB and WorkArena. Notably, we find that the fine-tuned agents' ability to predict expert trajectories does not consistently lead to improved downstream task performance. This raises issues such as off-policy bias and the loss of reasoning abilities during fine-tuning. We discuss potential solutions to these challenges and make both the codebase and a dataset of 140M tokens open-source for the community to build upon.

2024-10-21

NeurIPS.cc/2024/Workshop/OWA (poster)

AgentMerge: Enhancing Generalization in Fine-Tuned LLM Agents

Léo Boisvert

Alexandre Piché

Maxime Gasse

Alexandre Lacoste

Massimo Caccia

2024-10-09

NeurIPS.cc/2024/Workshop/AFM (poster)

Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs

Yash More

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Amal Zouaq

Payel Das

A. Chandar

Chandar Research Lab

Mila - Québec

U. Montŕeal

AI Institute

Ibm Research

Polytechnique Montréal

There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruc… (voir plus)tion-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.

2024-10-09

NeurIPS.cc/2024/Workshop/AFM (poster)

WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks

Léo Boisvert

Maxime Gasse

Massimo Caccia

Quentin Cappart

Nicolas Chapados

Alexandre Lacoste

The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents. Though recen… (voir plus)t LLMs seem capable of planning and reasoning given user instructions, their effectiveness in applying these capabilities for autonomous task solving remains underexplored. This is especially true in enterprise settings, where automated agents hold the promise of a high impact. To fill this gap, we propose WorkArena++, a novel benchmark consisting of 682 tasks corresponding to realistic workflows routinely performed by knowledge workers. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents. Our empirical studies across state-of-the-art LLMs and vision-language models (VLMs), as well as human workers, reveal several challenges for such models to serve as useful assistants in the workplace. In addition to the benchmark, we provide a mechanism to effortlessly generate thousands of ground-truth observation/action traces, which can be used for fine-tuning existing models. Overall, we expect this work to serve as a useful resource to help the community progress toward capable autonomous agents. The benchmark can be found at https://github.com/ServiceNow/WorkArena.

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?

Maxime Gasse

Massimo Caccia

Issam H. Laradji

Alexandre Lacoste

We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (voir plus)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 33 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques