Marc-Alexandre Côté

We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-… (see more)constrained environments. It is post-trained exclusively via RL on around 1,500 SWE environments with synthetic tasks. A key ingredient for improving performance is an online task synthesis pipeline that creates tasks calibrated to the frontier of learnability for the current checkpoint. This report provides evidence that competitive small coding agents can be trained with synthetic tasks alone, without traditional distillation from larger models, and that generating tasks at the learnability frontier of the current agent is important. We report details on the training methodology, evaluations across diverse environments, and in-depth analyses, serving as a foundation for our ongoing exploration of lightweight yet capable coding agents that can run on minimal hardware.

2026-09-06

arXiv (preprint)

Gistify: Codebase-Level Understanding via Runtime Execution

Hyunji Lee

Minseon Kim

Chinmay Singh

Matheus Pereira

Atharv Sonwane

Isadora White

Elias Stengel-Eskin

Mohit Bansal

Zhengxiang Shi

Eric Yuan

Lucas Caccia

As coding agents are increasingly deployed in large codebases, the need to automatically design challenging, codebase-level evaluation is ce… (see more)ntral. We propose Gistify, a task where a coding LLM must create a single, minimal, self-contained file that can reproduce a specific functionality of a codebase. The coding LLM is given full access to a codebase along with a specific entrypoint (e.g., a python command), and the generated file must replicate the output of the same command ran under the full codebase, while containing only the essential components necessary to execute the provided command. Success on Gistify requires both structural understanding of the codebase, accurate modeling of its execution flow as well as the ability to produce potentially large code patches. Our findings show that current state-of-the-art models struggle to reliably solve Gistify tasks, especially ones with long executions traces.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

openreview.net

Learning to Extract Context for Context-Aware LLM Inference

Minseon Kim

Lucas Caccia

Zhengyan Shi

Matheus Pereira

Xingdi Yuan

2025-12-11

ArXiv (preprint)

Gistify! Codebase-Level Understanding via Runtime Execution

Hyunji Lee

Minseon Kim

Chinmay Singh

Matheus Pereira

Atharv Sonwane

Isadora White

Elias Stengel-Eskin

Mohit Bansal

Zhengyan Shi

Xingdi Yuan

Lucas Caccia

As coding agents are increasingly deployed in large codebases, the need to automatically design challenging, codebase-level evaluation is ce… (see more)ntral. We propose Gistify, a task where a coding LLM must create a single, minimal, self-contained file that can reproduce a specific functionality of a codebase. The coding LLM is given full access to a codebase along with a specific entrypoint (e.g., a python command), and the generated file must replicate the output of the same command ran under the full codebase, while containing only the essential components necessary to execute the provided command. Success on Gistify requires both structural understanding of the codebase, accurate modeling of its execution flow as well as the ability to produce potentially large code patches. Our findings show that current state-of-the-art models struggle to reliably solve Gistify tasks, especially ones with long executions traces.

2025-10-29

ArXiv (preprint)

BugPilot: Complex Bug Generation for Efficient Learning of SWE Skills

Atharv Sonwane

Isadora White

Hyunji Lee

Matheus Pereira

Lucas Caccia

Minseon Kim

Zhengyan Shi

Chinmay Singh

Xingdi Yuan

2025-10-21

ArXiv (preprint)

Learning to Solve Complex Problems via Dataset Decomposition

Wanru Zhao

Lucas Caccia

Zhengyan Shi

Minseon Kim

Weijia Xu

Xingdi Yuan

2025-09-17

NeurIPS.cc/2025/Conference (poster)

openreview.net

debug-gym: A Text-Based Environment for Interactive Debugging

Xingdi Yuan

Morgane M Moss

Charbel Feghali

Chinmay Singh

Darya Moldavskaya

Drew MacPhee

Lucas Caccia

Matheus Pereira

Minseon Kim

2025-03-26

ArXiv (preprint)

Sub-Goal Distillation: A Method to Improve Small Language Agents

Maryam Hashemzadeh

Elias Stengel-Eskin

Sarath Chandar

While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational req… (see more)uirements and restricted number of calls constrain their practical utility, especially in long-horizon interactive tasks such as decision-making or in scenarios involving continuous ongoing tasks. To address these constraints, we propose a method for transferring the performance of an LLM with billions of parameters to a much smaller language model (770M parameters). Our approach involves constructing a hierarchical agent comprising a planning module, which learns through Knowledge Distillation from an LLM to generate sub-goals, and an execution module, which learns to accomplish these sub-goals using elementary actions. In detail, we leverage an LLM to annotate an oracle path with a sequence of sub-goals towards completing a goal. Subsequently, we utilize this annotated data to fine-tune both the planning and execution modules. Importantly, neither module relies on real-time access to an LLM during inference, significantly reducing the overall cost associated with LLM interactions to a fixed cost. In ScienceWorld, a challenging and multi-task interactive text environment, our method surpasses standard imitation learning based solely on elementary actions by 16.7% (absolute). Our analysis highlights the efficiency of our approach compared to other LLM-based methods. Our code and annotated data for distillation can be found on GitHub.

2025-02-16

Proceedings of The 3rd Conference on Lifelong Learning Agents (published)

proceedings.mlr.press

Language-guided Skill Learning with Temporal Variational Inference

Haotian Fu

Pratyusha Sharma

Elias Stengel-Eskin

George Konidaris

Nicolas Le Roux

Xingdi Yuan

We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose… (see more) an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. Our results demonstrate that agents equipped with our method are able to discover skills that help accelerate learning and outperform baseline skill learning approaches on new long-horizon tasks in BabyAI, a grid world navigation environment, as well as ALFRED, a household simulation environment.

2024-07-07

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

proceedings.mlr.press

Enhancing Agent Learning through World Dynamics Modeling

Xingdi Yuan

Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their i… (see more)mpressive performance largely attributed to the extensive domain knowledge embedded within them. However, the depth and breadth of this knowledge can vary across domains. Many existing approaches assume that LLMs possess a comprehensive understanding of their environment, often overlooking potential gaps in their grasp of actual world dynamics. To address this, we introduce Discover, Verify, and Evolve (DiVE), a framework that discovers world dynamics from a small number of demonstrations, verifies the accuracy of these dynamics, and evolves new, advanced dynamics tailored to the current situation. Through extensive evaluations, we assess the impact of each component on performance and compare the dynamics generated by DiVE to human-annotated dynamics. Our results show that LLMs guided by DiVE make more informed decisions, achieving rewards comparable to human players in the Crafter environment and surpassing methods that require prior task-specific training in the MiniHack environment.

2023-12-31

Findings of the Association for Computational Linguistics: EMNLP (published)

Joint Prompt Optimization of Stacked LLMs using Variational Inference

Xingdi Yuan

Matheus Pereira

Adam Trischler

Ziang Xiao

Arian Hosseini

Friederike Niedtner

Nicolas Le Roux

Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can b… (see more)e seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer. By stacking two such layers and feeding the output of one layer to the next, we obtain a Deep Language Network (DLN). We first show how to effectively perform prompt optimization for a 1-Layer language network (DLN-1). Then, we present an extension that applies to 2-layer DLNs (DLN-2), where two prompts must be learned. The key idea is to consider the output of the first layer as a latent variable, which requires inference, and prompts to be learned as the parameters of the generative distribution. We first test the effectiveness of DLN-1 in multiple reasoning and natural language understanding tasks. Then, we show that DLN-2 can reach higher performance than a single layer, showing promise that we might reach comparable performance to GPT-4, even when each LLM in the network is smaller and less powerful.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

openreview.net

From Words to Blocks: Building Objects by Grounding Language Models with Reinforcement Learning

Michael Ahn

Anthony Brohan

Noah Brown

liang Dai

Dan Su

Holy Lovenia Ziwei Bryan Wilie

Tiezheng Yu

Willy Chung

Quyet V. Do

Paul Barde

Tristan Karch

D. Nowrouzezahrai

C. Bonial

Mitchell Abrams

David R. Traum

Hyung Won

Le Hou

Shayne Longpre

Yi Zoph

William Tay … (see 32 more)

Eric Fedus

Xuezhi Li

Lasse Espeholt

Hubert Soyer

Remi Munos

Karen Si-801

Vlad Mnih

Tom Ward

Yotam Doron

Wenlong Huang

Pieter Abbeel

Deepak Pathak

Julia Kiseleva

Ziming Li

Mohammad Aliannejadi

Shrestha Mohanty

Maartje Ter Hoeve

Mikhail Burtsev

Alexey Skrynnik

Artem Zholus

A. Panov

Kavya Srinet

A. Szlam

Yuxuan Sun

Katja Hofmann