Combating Adversarial Inputs Using a Predictive-Estimator Network. - Jeff Orchard; Louis Castricato (ICONIP (2), 2017)

Towards a Formal Model of Narratives. - Louis Castricato; Stella Biderman; Rogelio E. Cardona-Rivera; David Thue (CoRR, 2021)

Fabula Entropy Indexing: Objective Measures of Story Coherence. - Louis Castricato; Spencer Frazier; Jonathan C. Balloch; Mark O. Riedl (CoRR, 2021)

Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representations. - Louis Castricato; Stephen Fitz; Won Young Shin (CoRR, 2021)

Cut the CARP: Fishing for zero-shot story evaluation. - Shahbuland Matiana; J. R. Smith; Ryan Teehan; Louis Castricato; Stella Biderman; Leo Gao; Spencer Frazier (CoRR, 2021)

Automated Story Generation as Question-Answering. - Louis Castricato; Spencer Frazier; Jonathan C. Balloch; Nitya Tarakad; Mark O. Riedl (CoRR, 2021)

VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance. - Katherine Crowson; Stella Biderman; Daniel Kornis; Dashiell Stander; Eric Hallahan; Louis Castricato; Edward Raff (ECCV (37), 2022)

EleutherAI: Going Beyond "Open Science" to "Science in the Open". - Jason Phang; Herbie Bradley; Leo Gao; Louis Castricato; Stella Biderman (CoRR, 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)

Linearly Mapping from Image to Text Space. - Jack Merullo; Louis Castricato; Carsten Eickhoff; Ellie Pavlick (ICLR, 2023)

Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning. - Joseph Suarez; David Bloomin; Kyoung Whan Choe; Hao Xiang Li; Ryan Sullivan; Nishaanth Kanna; Daniel Scott; Rose S. Shuman; Herbie Bradley; Louis Castricato; Phillip Isola; Chenghui Yu; et al. (NeurIPS, 2023)

Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning. - Joseph Suárez; Phillip Isola; Kyoung Whan Choe; David Bloomin; Hao Xiang Li; Nikhil Pinnaparaju; Nishaanth Kanna; Daniel Scott; Ryan Sullivan; Rose S. Shuman; Lucas de Alcântara; Herbie Bradley; et al. (CoRR, 2023)

Suppressing Pink Elephants with Direct Principle Feedback. - Louis Castricato; Nathan Lile; Suraj Anand; Hailey Schoelkopf; Siddharth Verma; Stella Biderman (CoRR, 2024)

Self-Directed Synthetic Dialogues and Revisions Technical Report. - Nathan Lambert 001; Hailey Schoelkopf; Aaron Gokaslan; Luca Soldaini; Valentina Pyatkin; Louis Castricato (CoRR, 2024)

Generative Reward Models. - Dakota Mahan; Duy Phung; Rafael Rafailov; Chase Blagden; Nathan Lile; Louis Castricato; Jan-Philipp Fränken; Chelsea Finn; Alon Albalak (CoRR, 2024)

PERSONA: A Reproducible Testbed for Pluralistic Alignment. - Louis Castricato; Nathan Lile; Rafael Rafailov; Jan-Philipp Fränken; Chelsea Finn (COLING, 2025)

Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought. - Violet Xiang; Charlie Snell; Kanishk Gandhi; Alon Albalak; Anikait Singh; Chase Blagden; Duy Phung; Rafael Rafailov; Nathan Lile; Dakota Mahan; Louis Castricato; Jan-Philipp Fränken; et al. (CoRR, 2025)

Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models. - Alon Albalak; Duy Phung; Nathan Lile; Rafael Rafailov; Kanishk Gandhi; Louis Castricato; Anikait Singh; Chase Blagden; Violet Xiang; Dakota Mahan; Nick Haber (CoRR, 2025)

Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning. - Joseph Suárez; Kyoung Whan Choe; David Bloomin; Jianming Gao; Yunkun Li; Yao Feng; Saidinesh Pola; Kun Zhang; Yonghui Zhu; Nikhil Pinnaparaju; Hao Xiang Li; Nishaanth Kanna; et al. (CoRR, 2025)