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- Reflexion: Language Agents with Verbal Reinforcement Learning
Concretely, Reflexion agents verbally reflect on task feedback signals, then maintain their own reflective text in an episodic memory buffer to induce better decision-making in subsequent trials
- Reflexion | Proceedings of the 37th International Conference on Neural . . .
Concretely, Reflexion agents verbally reflect on task feedback signals, then maintain their own reflective text in an episodic memory buffer to induce better decision-making in subsequent trials
- [NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement . . .
This repo holds the code, demos, and log files for Reflexion: Language Agents with Verbal Reinforcement Learning by Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao We have provided a set of notebooks to easily run, explore, and interact with the results
- 【AIGC论文阅读】Reflexion: Language Agents with Verbal Reinforcement Learning . . .
我们提出了Reflexion,这是一个新颖的框架,它不是通过更新权重来强化语言代理,而是通过语言反馈来实现。 具体来说,Reflexion代理会口头反思任务反馈信号,然后将它们自己的反思文本保留在情景记忆缓冲区中,以诱导在随后的尝试中做出更好的决策。
- Reflexion: Language Agents with Verbal Reinforcement Learning
创新点: 本文最核心的创新在于提出了 口头强化 (Verbal Reinforcement) 的概念。 它将传统 RL 中难以进行信用分配的稀疏标量奖励,转化为对 LLM 友好、信息密集的自然语言反馈。 这种反馈就像一种“语义梯度”,直接指导模型在下一次迭代中应该如何调整其行为
- Reflexion: language agents with verbal reinforcement learning . . .
Home Neural Information Processing Systems Foundation, Inc (NeurIPS) Reflexion: language agents with verbal reinforcement learning
- 论文理解【LLM-agent】—— 【Reflexion】Language Agents with Verbal Reinforcement . . .
我们提出了 Reflexion 框架,通过语言反馈而非权重更新,来强化 LM agent。 具体地说,Reflexion agent 对任务反馈信号进行自然语言反思,然后在情景记忆缓冲区(episodic memory)中保持它们自己的反思文本,以便在随后的 rollout 中做出更好的决策。
- Reflexion: Language Agents with Verbal Reinforcement Learning
This summary captures the agent’s reflections on its performance, highlighting what went wrong and suggesting improvements Updating Memory: The reflective summary
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