Apple Repurposes Large Language Models for Reinforcement Learning challenges in Embodied AI

An Apple research team presents Large LAnguage model Reinforcement Learning Policy (LLaRP). LLaRP effectively repurposes LLMs for Reinforcement Learning (RL) challenges within the realm of Embodied Artificial Intelligence (AI), achieving a remarkable 1.7 times higher success rate compared to other established baselines and zero-shot LLM applications.

ai apple applications artificial artificial intelligence challenges deep-neural-networks embodied embodied ai embodied intelligence intelligence language language model language models large language large language model large language models llm llm applications llms machine learning machine learning & data science ml policy rate reinforcement reinforcement leaerning reinforcement learning research research team success team technology