Esra'a Saleh

CURATE: Automatic Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search in Structured Task Spaces

Tabitha Edith Lee

Nan Rosemary Ke

Sarvesh Patil

Annya Dahmani

Eunice Yiu

Esra'a Saleh

Alison Gopnik

Oliver Kroemer

Glen Berseth

Due to fundamental exploration challenges without informed priors or specialized algorithms, agents may be unable to consistently receive in… (see more)formative rewards, leading to inefficient or intractable learning. To address these challenges, we introduce CURATE, an automatic curriculum learning algorithm for reinforcement learning agents in structured task spaces of monotonic difficulty. Through "exploration by exploitation," CURATE dynamically scales the task difficulty to match the agent's current competence. By exploiting its current capabilities that were learned in easier tasks, the agent improves its exploration in more difficult tasks. Our key insight is that the learning improvement in tasks that are close to those used for training is inversely proportional to their difficulty, and an agent that chooses a nearby distribution of the easiest unsolved tasks at any given time can automatically induce an easiest-to-hardest curriculum in these task spaces. To achieve this, CURATE conducts policy search in the task space to learn the best task distribution for training. As the agent's mastery grows, the learned curriculum adapts in an approximately easiest-to-hardest and task-directed fashion, efficiently culminating in a performant agent. Our experiments across three diverse domains (MiniGrid, Procgen, BipedalWalker) demonstrate that CURATE learns effective curricula for sample efficiency and proficiency with the potential for yielding broadly capable agents, matching or exceeding prior curriculum methods that do not require informed initialization or predefined schedules.

2026-08-30

Transactions on Machine Learning Research (accepted)

openreview.net

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts

Samin Yeasar Arnob

Zhan Su

Minseon Kim

Oleksiy Ostapenko

Riyasat Ohib

Esra’a Saleh

Doina Precup

Lucas Page-Caccia

Alessandro Sordoni

Merging parameter-efficient task experts has recently gained growing attention as a way to build modular architectures that can be rapidly a… (see more)dapted on the fly for specific downstream tasks, without requiring additional fine-tuning. Typically, LoRA (Low-Rank Adaptation) serves as the foundational building block of such parameter-efficient modular architectures, leveraging low-rank weight structures to reduce the number of trainable parameters. In this paper, we study the properties of sparse adapters, which train only a subset of weights in the base neural network, as potential building blocks of modular architectures. First, we propose a simple method for training highly effective sparse adapters, which is conceptually simpler than existing methods in the literature and surprisingly outperforms both LoRA and full fine-tuning in our setting. Next, we investigate the merging properties of these sparse adapters by merging adapters for up to 20 natural language processing tasks, thus scaling beyond what is usually studied in the literature. Our findings demonstrate that sparse adapters yield superior in-distribution performance post-merging compared to LoRA or full model merging. Achieving strong held-out performance remains a challenge for all methods considered.

2025-07-07

Conference on Language Modeling (accepted)

doi.org

openreview.net

Exploration by Exploitation: Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search

Tabitha Edith Lee

Nan Rosemary Ke

Sarvesh Patil

Annya Dahmani

Eunice Yiu

Esra'a Saleh

Alison Gopnik