Reflexion is an advanced framework for building autonomous agents powered by Large Language Models (LLMs), designed to overcome the limitations of single-pass reasoning. At its core, Reflexion allows an LLM agent to observe its own actions and the resulting outcomes, then generate reflective feedback based on these experiences. This feedback, stored in a memory buffer, is subsequently used to inform and improve the agent's future decision-making and planning processes. The mechanism involves an iterative loop where the agent attempts a task, evaluates its success or failure, reflects on 'why' it succeeded or failed, and then incorporates these insights to guide its next attempt or subsequent steps. This approach significantly enhances the agent's robustness, enables error recovery, and improves its ability to tackle complex, multi-step problems that require sustained reasoning and adaptation. Reflexion is primarily used by researchers in AI agents, reinforcement learning, cognitive architectures, and autonomous systems development, aiming to create more capable and reliable intelligent agents.
Reflexion is a method for AI agents to learn from their own experiences by reflecting on past actions and outcomes. This self-correction process helps them improve their performance on difficult tasks, making them more robust and capable of solving complex problems over time.
Self-Refine, Self-Correction, Self-Improvement, Cognitive Architectures for LLMs
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