LLMs Outperform Reinforcement Learning- Meet SPRING: An Innovative Prompting Framework for LLMs Designed to Enable in-Context Chain-of-Thought Planning and Reasoning

SPRING is an LLM-based policy that outperforms Reinforcement Learning algorithms in an interactive environment requiring multi-task planning and reasoning.  A group of researchers from Carnegie Mellon University, NVIDIA, Ariel University, and Microsoft have investigated the use of Large Language Models (LLMs) for understanding and reasoning with human knowledge in the context of games. They propose […]

The post LLMs Outperform Reinforcement Learning- Meet SPRING: An Innovative Prompting Framework for LLMs Designed to Enable in-Context Chain-of-Thought Planning and Reasoning appeared first …

ai shorts algorithms applications artificial intelligence carnegie mellon carnegie mellon university context editors pick environment framework interactive language language model language models large language large language model large language models llm llms machine learning microsoft nvidia planning policy prompt-engineering prompting reasoning reinforcement reinforcement learning researchers spring staff tech news technology thought university