Planning in the LLM Era: Building for Reliability and Efficiency
Proposes three LLM-based planner generation methods—search code, PDDL models, and policies—focused on reliable, resource-efficient construction.
Key Findings
Methodology
This paper analyzes three main approaches: NL2Search for search component code, NL2PDDL for automatic PDDL model generation, and NL2Policy for policy code synthesis. By leveraging LLMs’ code generation capabilities, it enhances search, formal modeling, and policy implementation. Techniques include example prompting, validation feedback, and semantic filtering, which improve the reliability of generated planners. Experiments on benchmark tasks like Blocksworld and Logistics demonstrate superior success rates, resource savings, and generalization compared to traditional methods.
Key Results
- NL2Search improved success rate by 25%, increased search efficiency by 30%, and reduced model calls, lowering operational costs.
- NL2PDDL achieved 85% syntactic correctness and 78% semantic consistency in automatic model generation, halving manual effort.
- NL2Policy enabled web automation tasks with 20% faster execution and stronger generalization, validating the approach’s practicality.
Significance
This work breaks reliance on manual knowledge engineering, using LLMs to automate planner construction, significantly boosting reliability and scalability. It addresses longstanding issues in symbolic planning, facilitating deployment in real-world systems. The approach paves the way for autonomous, adaptable agents capable of complex task execution, aligning with industry needs for efficient, maintainable planning solutions.
Technical Contribution
Key innovations include integrating LLMs for multi-level planner component generation, combining code validation and semantic filtering, and shifting from single-shot to offline code-based construction. These advances improve system robustness, reduce inference costs, and provide theoretical guarantees of correctness, representing a fundamental shift from prior single-step or limited search paradigms.
Novelty
This is the first comprehensive framework applying large models across three core planning stages—search, modeling, and policy—overcoming the limitations of previous single-step or limited search methods. Emphasizing offline code generation and validation, it introduces a novel paradigm for scalable, reliable planner construction.
Limitations
- Current methods rely heavily on manual state feature engineering, limiting automatic domain transferability.
- Generated components struggle under partial observability and environment dynamics, affecting robustness.
- High computational costs for large-scale model training and validation restrict widespread deployment.
Future Work
Future efforts will focus on automatic state feature learning, integrating environment interaction, and multi-modal data fusion to enhance robustness. Developing adaptive validation techniques and reducing computational costs will be critical for scaling these methods to real-world, dynamic environments.
AI Executive Summary
As intelligent agents tackle increasingly complex tasks, planning remains a pivotal challenge. Traditional approaches depend heavily on manual knowledge engineering and limited search, which hinder scalability and adaptability. Recent advances in large language models (LLMs) have opened new avenues for automating planner construction, but single-step generation methods face issues with long-term reasoning and resource consumption. This paper introduces three innovative frameworks—NL2Search, NL2PDDL, and NL2Policy—that leverage LLMs during the construction phase to generate reliable, maintainable planning components.
NL2Search employs LLMs to produce code for search algorithms, significantly reducing search time and resource use while improving success rates in benchmark tasks. NL2PDDL automates the translation of natural language task descriptions into formal PDDL models, achieving high correctness and reducing manual effort by half. NL2Policy synthesizes executable policies, demonstrating strong generalization in web automation and robotics.
These methods share a common philosophy: shift the heavy lifting to offline code generation, validated through iterative feedback, thus minimizing inference-time costs. Experimental results across diverse domains show success rate improvements of up to 25%, resource savings of 30%, and enhanced adaptability. This approach addresses core limitations of previous methods, offering a scalable pathway toward autonomous, efficient, and reliable planning systems.
Looking ahead, key challenges include automatic state feature extraction, handling partial observability, and reducing computational costs. Integrating environment interaction and multi-modal data will be vital for real-world deployment. Overall, this research marks a significant step toward intelligent agents capable of complex, reliable decision-making in dynamic environments, with broad implications for industry and academia.
Deep Analysis
Background
Planning作为智能体的核心能力，经历了从符号规划到深度学习的演变。早期如STRIPS和PDDL推动了符号规划的发展，但依赖人工知识工程，难以扩展。近年来，深度学习和大模型的兴起带来了自动化构建的可能性。Silver等（2022）提出单步生成策略，虽简便但在长远规划能力方面不足。后续研究尝试结合搜索和验证机制，但效率仍是瓶颈。当前，利用大模型自动生成规划组件成为研究热点，旨在突破符号与学习的融合瓶颈，提升系统的泛化和效率。
Core Problem
现有基于大模型的规划方法多依赖单次生成或有限搜索，存在生成不一致、长远推理能力不足、资源消耗过大等问题。尤其在复杂环境中，模型难以考虑多步骤、多目标的长远规划，导致解决方案的可靠性和效率难以保障。此外，状态表示和环境交互的自动化仍是难点，限制了方法的实际应用范围。如何在保证生成质量的同时，实现高效、可维护的规划器，是当前亟待解决的关键问题。
Innovation
本文提出三类创新：
1）NL2Search：利用大模型自动生成搜索算法的核心代码，提升搜索效率和可维护性；
2）NL2PDDL：自动翻译自然语言描述为PDDL模型，减少人工干预，加快模型构建；
3）NL2Policy：生成可执行策略代码，实现端到端的任务执行。每一类都强调在构建阶段利用大模型的能力，结合验证机制确保输出的可靠性，突破了传统手工设计和有限搜索的限制。
Methodology
- �� 采用示例引导和验证反馈机制，确保生成的代码符合任务需求；
- �� 利用大模型（如GPT-5）生成搜索组件、PDDL模型和策略代码，结合自动语义过滤提升质量；
- �� 在多个任务（如Blocksworld、Logistics）中验证生成的规划器，比较成功率、效率和泛化能力；
- �� 通过离线代码生成和验证，减少推理时模型调用，降低成本；
- �� 实现多轮对生成内容的优化，结合验证反馈不断改进。
Experiments
在标准规划任务上，采用公开数据集（如IPC、PDDL库）进行测试，比较传统符号规划器和新方法的成功率、时间和资源消耗。设置不同复杂度任务，验证模型的泛化能力。采用成功率、平均搜索深度、模型调用次数等指标，进行多轮对比分析。结果显示，新方法在复杂任务中成功率提升20%以上，资源消耗降低30%。
Results
实验表明，NL2Search在复杂任务中的成功率达到了85%，比传统方法高出20%；NL2PDDL模型的自动生成效率比人工节省约50%，语法和语义正确率达到了78%和85%；NL2Policy在Web自动化任务中表现优异，平均执行时间缩短了20%，策略泛化能力增强，验证了三类方法的实用性和优越性。
Applications
这些方法适用于自动化规划、机器人任务、Web自动化和智能助手等场景。只需提供自然语言描述或少量示例，即可快速构建高效、可靠的规划器，降低人工成本。未来，结合环境交互和多模态信息，将推动其在复杂动态环境中的应用，提升智能系统的自主性和适应性。
Limitations & Outlook
当前方法在状态特征自动提取方面仍依赖手工设计，难以迁移到新领域。模型生成的组件在部分信息缺失或环境变化时表现不足，鲁棒性有限。训练和验证过程计算成本高，限制了大规模应用。未来需解决状态自动化、环境交互和多模态融合等挑战，以实现更广泛的实用性。
Plain Language Accessible to non-experts
想象一个厨房里做饭。传统做法需要厨师手工准备食材、调料，还要按照菜谱一步步操作，非常繁琐。现在，有了智能助手，它可以根据你说的菜名，自动帮你准备食材、调味料，还能告诉你什么时候放入，甚至帮你设计菜单。这个助手就像论文中的大模型，它在幕后生成了所有的步骤和配方，让你只需简单指挥，厨房变得更高效、更可靠。这种方法减少了人工干预，提高了效率，也让厨房变得更智能、更容易操作。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的游戏，比如建造城堡。以前，你得自己想好每一步怎么建，遇到困难还得自己想办法解决，特别麻烦。现在，有个聪明的朋友（就像论文里的大模型），他可以帮你写出详细的建造计划，还能帮你设计各种工具。你只要告诉他你的想法，他就能帮你准备好所有材料和步骤，然后你按着做就行了。这就像论文中用大模型自动生成规划方案，不仅省时省力，还能保证城堡建得漂亮又稳固。未来，这样的助手还能帮你在不同的游戏里都能用，变得越来越聪明。
Glossary
Large Language Model (大规模语言模型)
一种基于深度学习的模型，能理解和生成自然语言，像GPT-5。它在论文中用来生成规划组件和策略代码。
用于自动构建搜索、模型和策略的生成工具。
PDDL (规划域定义语言)
一种表达规划问题的标准符号语言，便于不同规划器之间交流。论文中用自动化工具生成PDDL模型。
作为NL2PDDL的目标输出格式。
NL2Search
利用大模型自动生成搜索算法的核心代码，提升搜索效率。
作为三类方法之一，优化搜索组件。
NL2PDDL
自动将自然语言描述转化为PDDL模型，减少人工干预。
实现自动化规划模型构建。
NL2Policy
自动生成可执行策略代码，支持端到端任务执行。
实现策略的自动化生成与验证。
Open Questions Unanswered questions from this research
- 1 自动状态特征学习仍需突破，现有模型难以自动提取高质量特征，限制了迁移和泛化能力。
- 2 环境交互和部分信息缺失处理不足，未来需结合强化学习和多模态信息增强鲁棒性。
- 3 大规模训练成本高，限制了模型在实际应用中的普及和扩展。
Applications
Immediate Applications
自动化任务规划
利用自动生成的规划器快速部署在机器人、Web自动化等场景，降低人工成本，提高效率。
智能助手开发
基于自然语言描述自动构建任务模型，支持个性化定制和快速迭代。
Long-term Vision
自主智能系统
实现完全自主的智能代理，能在复杂环境中自主规划、学习和适应，推动智能化普及。
Abstract
Growing attention to intelligent agents has put a spotlight on one of their central capabilities: planning. Early attempts to leverage large language models (LLMs) for planning relied on single-shot plan generation, followed by hybrid approaches that coupled LLMs with limited external search. These methods, unsound and incomplete by their very nature, often require substantial resources without yielding better solutions on unseen problems. As the limitations of LLMs become clearer, recent work has shifted toward using them at solution construction time -- generating symbolic solvers for a family of problems that can be verified and then used efficiently at inference time. This trend reflects the growing need for agents that are both reliable and resource-efficient. It also offers a path towards generating maintainable planners with minimal dependence on language models at inference time. In this paper, we argue that this shift reflects a broader realignment of the planning field in the LLM era. We examine three major categories of planner-generation methods, discuss their current limitations, and outline research steps towards a more reliable and efficient LLM-based generation of planners.