Arash Ahmadian

According to our database, Arash Ahmadian authored at least 23 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2025

Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training.

Jonathan Cook, Silvia Sapora, Arash Ahmadian, Akbir Khan, Tim Rocktäschel, Jakob N. Foerster, Laura Ruis

CoRR, June, 2025

Calibrated Value-Aware Model Learning with Stochastic Environment Models.

Claas Voelcker, Anastasiia Pedan, Arash Ahmadian, Romina Abachi, Igor Gilitschenski, Amir-massoud Farahmand

CoRR, May, 2025

Aya Vision: Advancing the Frontier of Multilingual Multimodality.

Saurabh Dash, Yiyang Nan, John Dang, Arash Ahmadian, Shivalika Singh, Madeline Smith, Bharat Venkitesh, Vlad Shmyhlo, Viraat Aryabumi, Walter Beller-Morales, Jeremy Pekmez, Jason Ozuzu, Pierre Richemond, Acyr Locatelli, Nick Frosst, Phil Blunsom, Aidan N. Gomez, Ivan Zhang, Marzieh Fadaee, Manoj Govindassamy, Sudip Roy, Matthias Gallé, Beyza Ermis, Ahmet Üstün, Sara Hooker

CoRR, May, 2025

Command A: An Enterprise-Ready Large Language Model.

Aakanksha, Arash Ahmadian, Marwan Ahmed, Jay Alammar, Milad Alizadeh, Yazeed Alnumay, Sophia Althammer, Arkady Arkhangorodsky, Viraat Aryabumi, Dennis Aumiller, Raphaël Avalos, Zahara Aviv, Sammie Bae, Saurabh Baji, Alexandre Barbet, Max Bartolo, Björn Bebensee, Neeral Beladia, Walter Beller-Morales, Alexandre Bérard, Andrew Berneshawi, Anna Bialas, Phil Blunsom, Matt Bobkin, Adi Bongale, Sam Braun, Maxime Brunet, Samuel Cahyawijaya, David Cairuz, Jon Ander Campos, Cassie Cao, Kris Cao, Roman Castagné, Julián Cendrero, Leila Chan Currie, Yash Chandak, Diane Chang, Giannis Chatziveroglou, Hongyu Chen, Claire Cheng, Alexis Chevalier, Justin T. Chiu, Eugene Choi, Eujeong Choi, Tim Chung, Volkan Cirik, Ana Cismaru, Pierre Clavier, Henry Conklin, Lucas Crawhall-Stein, Devon Crouse, Felipe Cruz-Salinas, Ben Cyrus, Daniel D'souza, Hugo Dalla-Torre, John Dang, William Darling, Omar Darwiche Domingues, Saurabh Dash, Antoine Debugne, Théo Dehaze, Shaan Desai, Joan Devassy, Rishit Dholakia, Kyle Duffy, Ali Edalati, Ace Eldeib, Abdullah Elkady, Sarah Elsharkawy, Irem Ergün, Beyza Ermis, Marzieh Fadaee, Boyu Fan, Lucas Fayoux, Yannis Flet-Berliac, Nick Frosst, Matthias Gallé, Wojciech Galuba, Utsav Garg, Matthieu Geist, Mohammad Gheshlaghi Azar, Ellen Gilsenan-McMahon, Seraphina Goldfarb-Tarrant, Tomas Goldsack, Aidan N. Gomez, Victor Machado Gonzaga, Nithya Govindarajan, Manoj Govindassamy, Nathan Grinsztajn, Nikolas Gritsch, Patrick Gu, Shangmin Guo, Kilian Haefeli, Rod Hajjar, Tim Hawes, Jingyi He, Sebastian Hofstätter, Sungjin Hong

CoRR, April, 2025

Self-Improving Robust Preference Optimization.

Eugene Choi, Arash Ahmadian, Matthieu Geist, Olivier Pietquin, Mohammad Gheshlaghi Azar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Subspace learning for feature selection via rank revealing QR factorization: Fast feature selection.

Amir Moslemi, Arash Ahmadian

Expert Syst. Appl., 2024

Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier.

John Dang, Shivalika Singh, Daniel D'souza, Arash Ahmadian, Alejandro Salamanca, Madeline Smith, Aidan Peppin, Sungjin Hong, Manoj Govindassamy, Terrence Zhao, Sandra Kublik, Meor Amer, Viraat Aryabumi, Jon Ander Campos, Yi Chern Tan, Tom Kocmi, Florian Strub, Nathan Grinsztajn, Yannis Flet-Berliac, Acyr Locatelli, Hangyu Lin, Dwarak Talupuru, Bharat Venkitesh, David Cairuz, Bowen Yang, Tim Chung, Wei-Yin Ko, Sylvie Shang Shi, Amir Shukayev, Sammie Bae, Aleksandra Piktus, Roman Castagné, Felipe Cruz-Salinas, Eddie Kim, Lucas Crawhall-Stein, Adrien Morisot, Sudip Roy, Phil Blunsom, Ivan Zhang, Aidan N. Gomez, Nick Frosst, Marzieh Fadaee, Beyza Ermis, Ahmet Üstün, Sara Hooker

CoRR, 2024

If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs.

Muhammad Khalifa, Yi Chern Tan, Arash Ahmadian, Tom Hosking, Honglak Lee, Lu Wang, Ahmet Üstün, Tom Sherborne, Matthias Gallé

CoRR, 2024

Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning.

Aakanksha, Arash Ahmadian, Seraphina Goldfarb-Tarrant, Beyza Ermis, Marzieh Fadaee, Sara Hooker

CoRR, 2024

Averaging log-likelihoods in direct alignment.

Nathan Grinsztajn, Yannis Flet-Berliac, Mohammad Gheshlaghi Azar, Florian Strub, Bill Wu, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Olivier Pietquin, Matthieu Geist

CoRR, 2024

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion.

Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist

CoRR, 2024

Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning.

Ted Zadouri, Ahmet Üstün, Arash Ahmadian, Beyza Ermis, Acyr Locatelli, Sara Hooker

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion.

Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Bill Wu, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs.

John Dang, Arash Ahmadian, Kelly Marchisio, Julia Kreutzer, Ahmet Üstün, Sara Hooker

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm.

Aakanksha, Arash Ahmadian, Beyza Ermis, Seraphina Goldfarb-Tarrant, Julia Kreutzer, Marzieh Fadaee, Sara Hooker

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs.

Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, Sara Hooker

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023

Dual regularized subspace learning using adaptive graph learning and rank constraint: Unsupervised feature selection on gene expression microarray datasets.

Amir Moslemi, Arash Ahmadian

Comput. Biol. Medicine, December, 2023

Subspace learning using structure learning and non-convex regularization: Hybrid technique with mushroom reproduction optimization in gene selection.

Amir Moslemi, Mahdi Bidar, Arash Ahmadian

Comput. Biol. Medicine, September, 2023

λ-AC: Learning latent decision-aware models for reinforcement learning in continuous state-spaces.

Claas Voelcker, Arash Ahmadian, Romina Abachi, Igor Gilitschenski, Amir-massoud Farahmand

CoRR, 2023

Intriguing Properties of Quantization at Scale.

Arash Ahmadian, Saurabh Dash, Hongyu Chen, Bharat Venkitesh, Stephen Gou, Phil Blunsom, Ahmet Üstün, Sara Hooker

CoRR, 2023

Intriguing Properties of Quantization at Scale.

Arash Ahmadian, Saurabh Dash, Hongyu Chen, Bharat Venkitesh, Stephen Zhen Gou, Phil Blunsom, Ahmet Üstün, Sara Hooker

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Pseudo-Inverted Bottleneck Convolution for Darts Search Space.

Arash Ahmadian, Louis S. P. Liu, Yue Fei, Konstantinos N. Plataniotis, Mahdi S. Hosseini

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Subspace Learning for Feature Selection via Rank Revealing QR Factorization: Unsupervised and Hybrid Approaches with Non-negative Matrix Factorization and Evolutionary Algorithm.

Amir Moslemi, Arash Ahmadian

CoRR, 2022