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

GPT-4: Researchers from Carnegie Mellon University, NVIDIA, Ariel University, and Microsoft have developed SPRING, a Large Language Model (LLM)-based policy that outperforms Reinforcement Learning algorithms in complex game environments. By leveraging prior knowledge from academic papers and employing in-context chain-of-thought reasoning, SPRING surpasses state-of-the-art methods on the Crafter benchmark, achieving significant improvements in-game score and reward. The results highlight the potential of LLMs in complex tasks and suggest future advancements in visual-language models could address existing limitations.
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