Sheila A. McIlraith

Planning from Pixels using Inverse Dynamics Models

2020 · arXiv (Cornell University)

Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by learning to predict sequences of future actions conditioned on …

Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers

2025 · arXiv (Cornell University)

By utilizing more computational resources at test-time, large language models (LLMs) can improve without additional training. One common strategy uses verifiers to evaluate candidate outputs. In this work, we propose a novel scaling dimension for …