3 papers
cs.RO2026
Reinforcement Learning from Real-Robot Interaction without High-Quality State Estimates
Ryan Gao, Ashvin Anilkumar, Nicholas Corrado +2
A longstanding goal for robot reinforcement learning (RL) is that robots learn from their own physical experience, as humans and animals do. A key obstacle to learning directly fro…
cs.LG2026
Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
Nicholas E. Corrado, Wenyuan Huang, Josiah P. Hanna
Multi-task reinforcement learning (MTRL) aims to train a single agent to efficiently optimize performance across multiple tasks simultaneously. However, jointly optimizing all task…
cs.LG2025
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs
Nicholas E. Corrado, Julian Katz-Samuels, Adithya Devraj +6
When aligning large language models (LLMs), their performance on various tasks (such as being helpful, harmless, and honest) depends heavily on the composition of their training da…