让大模型当总指挥，规划长期个人目标并动态调整。
Conversational Planning for Personal Plans
- 大模型做宏观决策，工具调用执行具体任务
- 通过对话收集反馈，实现长期计划自适应调整
- 适合教育辅导与健康管理等持续性场景
大型语言模型（LLMs）的语言生成与推理能力已使对话系统在代码生成、作文撰写、通过理工科和法律考试、知识搜索等短期任务中表现出色。如今，这些模型越来越多地被用于帮助完成需数天、数周、数月甚至数年才能完成的真实生活目标，涉及多轮跨时段交互。为此，需要具备长期规划能力的语言代理。传统方法依赖具有层次化规划能力的强化学习代理。本文提出新架构：由大模型充当元控制器，决定代理的下一步宏观动作；工具增强的基于大模型的选项策略则执行选定的宏观动作。我们在此框架下实例化一组宏观动作，支持通过对话与后续提问收集用户反馈，实现对个人计划的自适应规划。该范式可应用于从学术与非学术任务辅导到个人健康计划对话教练等多种场景。
原文摘要 · Abstract (English)
The language generation and reasoning capabilities of large language models (LLMs) have enabled conversational systems with impressive performance in a variety of tasks, from code generation, to composing essays, to passing STEM and legal exams, to a new paradigm for knowledge search. Besides those short-term use applications, LLMs are increasingly used to help with real-life goals or tasks that take a long time to complete, involving multiple sessions across days, weeks, months, or even years. Thus to enable conversational systems for long term interactions and tasks, we need language-based agents that can plan for long horizons. Traditionally, such capabilities were addressed by reinforcement learning agents with hierarchical planning capabilities. In this work, we explore a novel architecture where the LLM acts as the meta-controller deciding the agent's next macro-action, and tool use augmented LLM-based option policies execute the selected macro-action. We instantiate this framework for a specific set of macro-actions enabling adaptive planning for users' personal plans through conversation and follow-up questions collecting user feedback. We show how this paradigm can be applicable in scenarios ranging from tutoring for academic and non-academic tasks to conversational coaching for personal health plans.
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