30 citations · 69 across the 17 of their papers we have counts for
19 papers
Waypoint-1.5: A Real-Time Video World Model for Consumer Hardware
Rajit Rajpal, Shahbuland Matiana, Liew Wei Pyn +13
We present Waypoint 1.5, a real-time diffusion world model for interactive video generation on consumer-grade hardware. Unlike general video diffusion models, interactive world mod…
COMBAT: Conditional World Models for Behavioral Agent Training
Anmol Agarwal, Pranay Meshram, Sumer Singh +5
Recent advances in video generation have spurred the development of world models capable of simulating 3D-consistent environments and interactions with static objects. However, a s…
Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning
Joseph Suárez, Kyoung Whan Choe, David Bloomin +22
We present the results of the NeurIPS 2023 Neural MMO Competition, which attracted over 200 participants and submissions. Participants trained goal-conditional policies that genera…
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Alon Albalak, Duy Phung, Nathan Lile +8
Increasing interest in reasoning models has led math to become a prominent testing ground for algorithmic and methodological improvements. However, existing open math datasets eith…
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
Violet Xiang, Charlie Snell, Kanishk Gandhi +11
We propose a novel framework, Meta Chain-of-Thought (Meta-CoT), which extends traditional Chain-of-Thought (CoT) by explicitly modeling the underlying reasoning required to arrive…
Generative Reward Models
Dakota Mahan, Duy Van Phung, Rafael Rafailov +6
Reinforcement Learning from Human Feedback (RLHF) has greatly improved the performance of modern Large Language Models (LLMs). The RLHF process is resource-intensive and technicall…
Self-Directed Synthetic Dialogues and Revisions Technical Report
Nathan Lambert, Hailey Schoelkopf, Aaron Gokaslan +3
Synthetic data has become an important tool in the fine-tuning of language models to follow instructions and solve complex problems. Nevertheless, the majority of open data to date…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…
Suppressing Pink Elephants with Direct Principle Feedback
Louis Castricato, Nathan Lile, Suraj Anand +3
Existing methods for controlling language models, such as RLHF and Constitutional AI, involve determining which LLM behaviors are desirable and training them into a language model.…
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
Joseph Suárez, Phillip Isola, Kyoung Whan Choe +15
Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define…
EleutherAI: Going Beyond "Open Science" to "Science in the Open"
Jason Phang, Herbie Bradley, Leo Gao +2
Over the past two years, EleutherAI has established itself as a radically novel initiative aimed at both promoting open-source research and conducting research in a transparent, op…
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning
Louis Castricato, Alexander Havrilla, Shahbuland Matiana +5
Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. Existing methods to control…
Linearly Mapping from Image to Text Space
Jack Merullo, Louis Castricato, Carsten Eickhoff +1
The extent to which text-only language models (LMs) learn to represent features of the non-linguistic world is an open question. Prior work has shown that pretrained LMs can be tau…
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance
Katherine Crowson, Stella Biderman, Daniel Kornis +4
Generating and editing images from open domain text prompts is a challenging task that heretofore has required expensive and specially trained models. We demonstrate a novel method…
Automated Story Generation as Question-Answering
Louis Castricato, Spencer Frazier, Jonathan Balloch +2
Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work towar…
Cut the CARP: Fishing for zero-shot story evaluation
Shahbuland Matiana, JR Smith, Ryan Teehan +4
Recent advances in large-scale language models (Raffel et al., 2019; Brown et al., 2020) have brought significant qualitative and quantitative improvements in machine-driven text g…
Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representations
Louis Castricato, Stephen Fitz, Won Young Shin
As the computational footprint of modern NLP systems grows, it becomes increasingly important to arrive at more efficient models. We show that by employing graph convolutional docu…
Fabula Entropy Indexing: Objective Measures of Story Coherence
Louis Castricato, Spencer Frazier, Jonathan Balloch +1
Automated story generation remains a difficult area of research because it lacks strong objective measures. Generated stories may be linguistically sound, but in many cases suffer…
Towards a Formal Model of Narratives
Louis Castricato, Stella Biderman, Rogelio E. Cardona-Rivera +1
In this paper, we propose the beginnings of a formal framework for modeling narrative \textit{qua} narrative. Our framework affords the ability to discuss key qualities of stories…