Does Reflexion's verbal reinforcement learning generalize to multimodal agents on the ALFRED benchmark for emb

Abstract

Abstract: Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning methods require extensive training samples and expensive model fine-tuning. We propose Reflexion, a novel framework to reinforce language agents not by updating weights, but instead through linguistic feedback. Concretely, Reflexion agents verbally reflect on task feedback signals, then maintain the

Research Question

Does Reflexion's verbal reinforcement learning generalize to multimodal agents on the ALFRED benchmark for embodied task completion, measured by success rate and subgoal accuracy?

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