Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks

2025-03-12

AI Summary

To address the weak planning capability and low plan accuracy of large language models (LLMs) in long-horizon, multi-step tasks, this paper proposes a planner-executor decoupled two-stage framework: a Planner generates structured high-level plans, while an Executor performs environment-specific actions. We introduce an explicit planning augmentation paradigm, designing a scalable synthetic data generation method to construct diverse plan trajectories with ground-truth annotations. By integrating trajectory alignment annotation, synthetic data distillation, and generalization-enhanced training, we significantly improve planning robustness. Evaluated on the WebArena-Lite benchmark, our approach achieves a 54% task success rate—setting a new state-of-the-art for long-horizon web navigation—and establishes a novel paradigm for reliable long-term planning in LLM-based agents.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large language models (LLMs) have shown remarkable advancements in enabling language agents to tackle simple tasks. However, applying them for complex, multi-step, long-horizon tasks remains a challenge. Recent work have found success by separating high-level planning from low-level execution, which enables the model to effectively balance high-level planning objectives and low-level execution details. However, generating accurate plans remains difficult since LLMs are not inherently trained for this task. To address this, we propose Plan-and-Act, a novel framework that incorporates explicit planning into LLM-based agents and introduces a scalable method to enhance plan generation through a novel synthetic data generation method. Plan-and-Act consists of a Planner model which generates structured, high-level plans to achieve user goals, and an Executor model that translates these plans into environment-specific actions. To train the Planner effectively, we introduce a synthetic data generation method that annotates ground-truth trajectories with feasible plans, augmented with diverse and extensive examples to enhance generalization. We evaluate Plan-and-Act using web navigation as a representative long-horizon planning environment, demonstrating a state-of the-art 54% success rate on the WebArena-Lite benchmark.

Problem

• Improving planning for long-horizon tasks using LLMs
• Enhancing plan generation with synthetic data
• Achieving high success rates in web navigation tasks

Innovation

• Incorporates explicit planning into LLM-based agents
• Introduces scalable synthetic data generation method
• Separates high-level planning from low-level execution