Research Engineer
Meta Superintelligence Labs
prajj at meta dot com

Bio

I’m Praj, I work as an AI Researcher at Meta Superintelligence Labs working on building foundational models. I am a core contributor of LLaMA 4, LLaMA 3 LLaMA 2, LLaMA 2 Long, powering Meta’s flagship AI assistant meta.ai. My primary focus has been on fundamental pre-training research and infra around it. One of my primary contributions include building long context capabilites for LLaMA 4 both on modeling and pre-training infra / inference. LLaMA 4 can attend to documents exceeding 10M tokens. Previously I worked as an AI Resident within Reality Labs and Fundamental AI Research (FAIR) at Meta working on Offline Reinforcement Learning. My google scholar can be found here.

Prior to Meta, I was a CS graduate student at the University of Texas Dallas where I worked on commonsense reasoning under the supervision of Prof. Vincent Ng. My thesis is about improving commonsense reasoning through adversarial learning.

Publications


BTS: Harmonizing Specialized Experts into a Generalist LLM

Qizhen Zhang, Prajjwal Bhargava, Chloe Bi, Chris X. Cai, Jakob Foerster, Jeremy Fu, Punit Singh Koura, Ruan Silva, Sheng Shen, Emily Dinan, Suchin Gururangan, Mike Lewis
Paper


The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation

Generative AI, Meta
Paper


The LLaMA 3 herd of models

Generative AI, Meta
Paper


Correlating and Predicting Human Evaluations of Language Models from NLP Benchmarks

Rylan Schaeffer, Punit Singh Koura, Binh Tang, Ranjan Subramanian, Aaditya K Singh, Todor Mihaylov, Prajjwal Bhargava, Lovish Madaan, Niladri S. Chatterji, Vedanuj Goswami, Sergey Edunov, Dieuwke Hupkes, Sanmi Koyejo, Sharan Narang
Paper


Effective Long-Context Scaling of Foundation Models

W. Xiong, J. Liu, I. Molybog, H. Zhang, P. Bhargava , R. Hou, L. Martin, R. Rungta, K. Sankararaman, B. Oguz, M. Khabsa, H. Fang, Y. Mehdad, S. Narang, K. Malik, A. Fan, S. Bhosale, S. Edunov, M. Lewis, S. Wang, H. Ma
Paper


Llama 2: Open Foundation and Fine-Tuned Chat Models

H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava , S. Bhosale, D. Bikel, L. Blecher, C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P. Koura, M. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. Smith, R. Subramanian, X. Tan, B. Tang, R. Taylor, A. Williams, J. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, T. Scialom
Paper Official Announcement Code


Sequence Modeling is a Robust Contender for Offline Reinforcement Learning

Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang
International Conference on Learning Representations (ICLR) 2024 arXiv Code Bibtex


AUTODIAL: Efficient Asynchronous Task-Oriented Dialogue Model

Prajjwal Bhargava, Pooyan Amini, Shahin Shayandeh, Chinnadhurai Sankar
arXiv Code Bibtex


DiscoSense: Commonsense Reasoning with Discourse Relations

Prajjwal Bhargava and Vincent Ng
EMNLP 2022 arXiv Code Bibtex


Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey

Prajjwal Bhargava and Vincent Ng
AAAI 2022 Paper Poster Bibtex


Generalization in NLI: Ways to [Not] Go Beyond Simple Heuristics

Prajjwal Bhargava, Aleksander Drozd, Anna Rogers
EMNLP Workshop on Insights from Negative Results 2022 Paper Code (Huggingface) Code (Pytorch Lightning) Bibtex Presentation video Poster Slides


Adaptive Transformers for Learning Multimodal Representations

Prajjwal Bhargava
ACL SRW 2022 Paper Code Bibtex Presentation Video


On Generalization of Detection Models for Unconstrained Environments

Prajjwal Bhargava
ICCV AutoNUE Workshop 2022 Paper Code Bibtex Poster


Incremental Learning in Person Re-Identification

Prajjwal Bhargava
arXiv preprint Paper Code Bibtex Poster


Side projects

fluence

Winner of Pytorch Global Hackathon 2020. A Pytorch deep learning library focussed on providing support for compute efficient and debiasing algorithms in transformer based model for NLP research. Contains implementation of Adaptive Attention, Sparsity, Layerdrop, Debiasing, Pruning utilities etc.

Open source contributions

Contributions to Pytorch Ecosystem

Autonomous Object Detection

This project focussed on 2D object detection with Pytorch. User can leverage models provided from `torchvision` and use datasets provided in this project (`idd`, `cityscapes`, `bdd`) for training and evaluation of models. Additionally, support for incremental learning was added.