Aleksei Petrenko

Agents that Listen: High-Throughput Reinforcement Learning with Multiple\n Sensory Systems

2021 · arXiv (Cornell University)

Humans and other intelligent animals evolved highly sophisticated perception\nsystems that combine multiple sensory modalities. On the other hand,\nstate-of-the-art artificial agents rely mostly on visual inputs or structured\nlow-dimensional observations provided by instrumented environments. Learning to\nact based …

Reinforcement Learning for Long-Horizon Interactive LLM Agents

2025 · arXiv (Cornell University)

Interactive digital agents (IDAs) leverage APIs of stateful digital environments to perform tasks in response to user requests. While IDAs powered by instruction-tuned large language models (LLMs) can react to feedback from interface invocations in …