What is the impact of fine-tuning on negative interaction trajectories versus positive-only trajectories for L

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

What is the impact of fine-tuning on negative interaction trajectories versus positive-only trajectories for LLM agent tool-use accuracy on the WebShop benchmark, measured by success rate and average reward?

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