Portrait of Maryam Hashemzadeh is unavailable

Maryam Hashemzadeh

maryam.hashemzadeh@mila.quebec
PhD - Université de Montréal
Supervisor
Sarath Chandar
Research Topics
Deep Learning
Reinforcement Learning
Website
Google Scholar

Publications

FrogNano: Training a 4B Coding Agent via Online Task Synthesis
Minseon Kim
Zhengyan Shi
Emiliano Penaloza
Christopher Cui
Roger Creus Castanyer
Maryam Hashemzadeh
Isadora White
Jonathan Light
Jeonghye Kim
Matheus Pereira
Darya Moldavskaya
Chinmay Singh
Fabio Vera
Baolin Peng
Xingdi Yuan
Marc-Alexandre Côté
Alessandro Sordoni
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)
doi.org
arxiv.org
Probabilistic Calibration Is a Trainable Capability in Language Models
Davide Baldelli
Sruthi Kuriakose
Maryam Hashemzadeh
Amal Zouaq
Sarath Chandar
Language models are increasingly used in settings where outputs must satisfy user-specified randomness constraints, yet their generation pro… (see more)babilities are often poorly calibrated to those targets. We study whether this capability can be improved directly through fine-tuning. Concretely, we fine-tune language models on synthetic prompts that require sampling from mathematical distributions, and compare two Calibration Fine-Tuning variants: a soft-target method that converts the desired output distribution into trie-derived next-token targets, and a hard-target method that trains on sampled completions from the same target distribution. Across 12 models spanning four families, both methods substantially improve structured-sampling fidelity on held-out distribution families and unseen parameter settings, showing that probabilistic calibration is a trainable capability. Under our selected training configurations, the two methods exhibit different empirical profiles: hard-target fine-tuning is often strongest on structured numeric sampling, while soft-target fine-tuning performs better on broader stochastic generation benchmarks, including open-ended random generation, multiple-choice answer-position balancing, and NoveltyBench. The gains sometimes reduce downstream capability, especially arithmetic reasoning, with costs varying by model. Overall, our results show that probabilistic calibration can be improved through fine-tuning, with our hard-target configuration favoring exact numeric fidelity and our soft-target configuration favoring broader stochastic transfer. Code is available at https://github.com/chandar-lab/calibration-finetuning.
2026-05-11
arXiv (preprint)
doi.org
arxiv.org
Sub-Goal Distillation: A Method to Improve Small Language Agents
Maryam Hashemzadeh
Elias Stengel-Eskin
Sarath Chandar
Marc-Alexandre Côté
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)
doi.org
proceedings.mlr.press