3 papers
cs.LG2026
When Do Intrinsic Rewards Lead to Exploration?
Scott W. Viteri, Laura Gomezjurado Gonzalez, Clark Barrett
Intrinsic rewards are designed to guide exploration in reinforcement learning by assigning value to an agent's experience, for example through prediction error or learning progress…
cs.RO2026
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
Jadelynn Dao, Milan Ganai, Yasmina Abukhadra +7
Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. Ho…
cs.CL2024
Markovian Transformers for Informative Language Modeling
Scott Viteri, Max Lamparth, Peter Chatain +1
Chain-of-Thought (CoT) reasoning often fails to faithfully reflect a language model's underlying decision process. We address this by introducing a Markovian language model framewo…