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Samin Yeasar Arnob

PhD - McGill University
Supervisor
Doina Precup
Research Topics
Reinforcement Learning
Website
Google Scholar

Publications

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 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.
2025-07-06
colmweb.org/COLM/2025/Conference (accepted)
doi.org
openreview.net
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
Samin Yeasar Arnob
Scott Fujimoto
Doina Precup
In this paper, we investigate the use of small datasets in the context of offline reinforcement learning (RL). While many common offline RL … (see more)benchmarks employ datasets with over a million data points, many offline RL applications rely on considerably smaller datasets. We show that offline RL algorithms can overfit on small datasets, resulting in poor performance. To address this challenge, we introduce"Sparse-Reg": a regularization technique based on sparsity to mitigate overfitting in offline reinforcement learning, enabling effective learning in limited data settings and outperforming state-of-the-art baselines in continuous control.
2025-05-31
arXiv (published)
doi.org
arxiv.org
Efficient Reinforcement Learning by Discovering Neural Pathways
Samin Yeasar Arnob
Riyasat Ohib
Sergey Plis
Amy Zhang
Alessandro Sordoni
Doina Precup
2023-12-31
Advances in Neural Information Processing Systems 37 (published)
doi.org
openreview.net
Offline Policy Optimization in RL with Variance Regularizaton
Riashat Islam
Samarth Sinha
Homanga Bharadhwaj
Samin Yeasar Arnob
Zhuoran Yang
Animesh Garg
Zhaoran Wang
Lihong Li
Doina Precup
2022-12-28
ArXiv (preprint)
doi.org
arxiv.org
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
Samin Yeasar Arnob
Riashat Islam
Doina Precup
2021-12-30
ArXiv (preprint)
arxiv.org
arxiv.org
Single-Shot Pruning for Offline Reinforcement Learning
Samin Yeasar Arnob
Riyasat Ohib
Sergey Plis
Doina Precup
2021-12-30
ArXiv (preprint)
arxiv.org
arxiv.org
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning
Riashat Islam
Raihan Seraj
Samin Yeasar Arnob
Doina Precup
We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor… (see more)-critic algorithms require an off-policy critic evaluation step, to estimate the value of the new policy after every policy gradient update. Despite enormous success of off-policy policy gradients on control tasks, existing general methods suffer from high variance and instability, partly because the policy improvement depends on gradient of the estimated value function. In this work, we present a new way of off-policy policy evaluation in actor-critic, based on the doubly robust estimators. We extend the doubly robust estimator from off-policy policy evaluation (OPE) to actor-critic algorithms that consist of a reward estimator performance model. We find that doubly robust estimation of the critic can significantly improve performance in continuous control tasks. Furthermore, in cases where the reward function is stochastic that can lead to high variance, doubly robust critic estimation can improve performance under corrupted, stochastic reward signals, indicating its usefulness for robust and safe reinforcement learning.
2019-12-09
arXiv (preprint)
doi.org
arxiv.org