Reflexion: Language Agents with Verbal Reinforcement Learning

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After each trajectory, an evaluator produces a reward or error signal, and the agent generates a natural-language reflection on what went wrong, which is appended to an episodic memory buffer that conditions subsequent trials. Because learning happens through text in the context window rather than gradient updates, the same frozen model improves across attempts on decision-making, coding, and reasoning benchmarks. This provided a cheap iterative-improvement mechanism for agents that reuses a model's own language ability as the update rule.