Archit Sharma

Robots are learning to adapt and perform tasks across various fields.

Robots learn to adapt and improve by receiving real-time human feedback.

A new framework helps language models learn from mistakes in problem-solving.

This study evaluates methods to enhance large language models using user preference data.

This article examines key factors in preference dataset quality for better reward model training.

Discover how Policy Agnostic Reinforcement Learning changes machine decision-making.