用反馈搜索优化大模型生成规划领域的质量。
Model Space Reasoning as Search in Feedback Space for Planning Domain Generation
- 通过符号化反馈引导模型空间搜索，提升领域生成质量。
- 使用地标和验证器输出作为反馈，显著改善生成效果。
- 适合对自动化规划系统构建感兴趣的开发者与研究者。
尽管大型语言模型和推理模型已出现，从自然语言描述生成规划领域仍是开放问题。现有研究表明，尽管LLM能辅助领域生成，但其产出的高质量可部署领域仍不足。为此，我们研究了一种基于代理语言模型的反馈框架，利用少量符号信息增强自然语言描述，以生成规划领域。具体评估了包括地标和VAL计划验证器输出在内的多种符号反馈形式，并通过在模型空间中进行启发式搜索来优化领域质量。
原文摘要 · Abstract (English)
The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasoning models. Recent work suggests that while LLMs have the ability to assist with domain generation, they are still far from producing high quality domains that can be deployed in practice. To this end, we investigate the ability of an agentic language model feedback framework to generate planning domains from natural language descriptions that have been augmented with a minimal amount of symbolic information. In particular, we evaluate the quality of the generated domains under various forms of symbolic feedback, including landmarks, and output from the VAL plan validator. Using these feedback mechanisms, we experiment using heuristic search over model space to optimize domain quality.
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