Alexander Havrilla

According to our database, Alexander Havrilla authored at least 12 papers between 2022 and 2024.

Collaborative distances:

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

2024

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models.

Alex Havrilla, Andrew Dai, Laura O'Mahony, Koen Oostermeijer, Vera Zisler, Alon Albalak, Fabrizio Milo, Sharath Chandra Raparthy, Kanishk Gandhi, Baber Abbasi, Duy Phung, Maia Iyer, Dakota Mahan, Chase Blagden, Srishti Gureja, Mohammed Hamdy, Wen-Ding Li, Giovanni Paolini, Pawan Sasanka Ammanamanchi, Elliot Meyerson

CoRR, 2024

Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data.

Alex Havrilla, Wenjing Liao

CoRR, 2024

Teaching Large Language Models to Reason with Reinforcement Learning.

Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Sainbayar Sukhbaatar, Roberta Raileanu

CoRR, 2024

GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements.

Alex Havrilla, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Roberta Railneau

CoRR, 2024

Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought.

Alex Havrilla, Maia Iyer

CoRR, 2024

DFU: scale-robust diffusion model for zero-shot super-resolution image generation.

Alexander Havrilla, Kevin Rojas, Wenjing Liao, Molei Tao

CoRR, 2024

GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements.

Alexander Havrilla, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Roberta Raileanu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

ARB: Advanced Reasoning Benchmark for Large Language Models.

Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John J. Nay, Kshitij Gupta, Aran Komatsuzaki

CoRR, 2023

Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness.

Hao Liu, Alexander Havrilla, Rongjie Lai, Wenjing Liao

CoRR, 2023

trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback.

Alexander Havrilla, Maksym Zhuravinskyi, Duy Phung, Aman Tiwari, Jonathan Tow, Stella Biderman, Quentin Anthony, Louis Castricato

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning.

Louis Castricato, Alexander Havrilla, Shahbuland Matiana, Michael Pieler, Anbang Ye, Ian Yang, Spencer Frazier, Mark O. Riedl

CoRR, 2022

On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds.

Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022