On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. - Biraj Dahal; Alexander Havrilla; Minshuo Chen; Tuo Zhao; Wenjing Liao (NeurIPS, 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)

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 (EMNLP, 2023)

Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness. - Hao Liu 028; Alexander Havrilla; Rongjie Lai; Wenjing Liao (CoRR, 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)

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 (ICML, 2024)

Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data. - Alexander Havrilla; Wenjing Liao (NeurIPS, 2024)

DFU: scale-robust diffusion model for zero-shot super-resolution image generation. - Alexander Havrilla; Kevin Rojas; Wenjing Liao; Molei Tao (CoRR, 2024)

Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought. - Alex Havrilla; Maia Iyer (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)

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)

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models. - Alex Havrilla; Andrew Dai 001; Laura O'Mahony; Koen Oostermeijer; Vera Zisler; Alon Albalak; Fabrizio Milo; Sharath Chandra Raparthy; Kanishk Gandhi; Baber Abbasi; Duy Phung; Maia Iyer; et al. (CoRR, 2024)

IGDA: Interactive Graph Discovery through Large Language Model Agents. - Alex Havrilla; David Alvarez-Melis; Nicolò Fusi (CoRR, 2025)

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights. - Zhaiming Shen; Alex Havrilla; Rongjie Lai; Alexander Cloninger; Wenjing Liao (CoRR, 2025)

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms. - Alex Havrilla; Edward Hughes 001; Mikayel Samvelyan; Jacob D. Abernethy (CoRR, 2025)