Jeffrey Wu

Fine-Tuning Language Models from Human Preferences

2019 · arXiv (Cornell University)

Reward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions. Most work on reward learning has used simulated …

Scaling Laws for Neural Language Models

2020 · arXiv (Cornell University)

This paper develops a transport-validity theory for agentic AI interventions that are first screened on small systems and later considered for frontier-scale deployment. Rather than predicting absolute frontier performance, it asks when a comparative gain …

Language Models are Few-Shot Learners

2020 · arXiv (Cornell University)

Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still …