https://arxiv.org/abs/2303.11366
https://arxiv.org/abs/2303.11366
Reflexion: an autonomous agent with dynamic memory and self-reflectionRecent advancements in decision-making large language model (LLM) agents have demonstrated impressive performance across various benchmarks. However, these state-of-the-art approaches typically necessitate internal model fine-tuning, external model fine-tuning, or policy optimization over a defined state space. Implementing these methods can prove challenging due to the scarcity of high-quality training data or the lack of well-defined state space. Moreover, these agents do not possess certain qualities inherent to human decision-making processes, specifically the ability to learn from mistakes. Self-reflection allows humans to efficiently solve novel problems through a process of trial and error. Building on recent research, we propose Reflexion, an approach that endows an agent with dynamic memory and self-reflection capabilities to enhance its existing reasoning trace and task-specific action choice abilities. To achieve full automation, we introduce a straightforward yet effective heuristic that enables the agent to pinpoint hallucination instances, avoid repetition in action sequences, and, in some environments, construct an internal memory map of the given environment. To assess our approach, we evaluate the agent's ability to complete decision-making tasks in AlfWorld environments and knowledge-intensive, search-based question-and-answer tasks in HotPotQA environments. We observe success rates of 97% and 51%, respectively, and provide a discussion on the emergent property of self-reflection.arxiv.orghttps://nanothoughts.substack.com/p/reflecting-on-reflexion
https://nanothoughts.substack.com/p/reflecting-on-reflexion
Reflecting on ReflexionIt’s not everyday that humans develop novel techniques to achieve state-of-the-art standards using decision-making processes once thought to be unique to human intelligence. But, that’s exactly what we did: here is a discussion on our recent paper with an additional experiment using GPT-4 to beat past GPT-4 standards.nanothoughts.substack.com
페이퍼라 의미없엉 어짜피 gpt5에서 이것저것 다 적용해서 코딩도 90프로 찍을것
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https://www.reddit.com/r/MachineLearning/comments/1215dbl/r_reflexion_an_autonomous_agent_with_dynamic/
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