InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning

Abstract: Recent advancements in LLMs have enabled their use as agents for planning complex tasks. Existing methods typically rely on a thought-action-observation (TAO) process to enhance LLM performance, but these approaches are often constrained by the LLMs' limited knowledge of complex tasks. Retrieval-augmented generation (RAG) offers new opportunities by leveraging external databases to ground generation in retrieved information. In this paper, we identify two key challenges (enlargability and transferability) in applying RAG to task planning. We propose InstructRAG, a novel solution within a multi-agent meta-reinforcement learning framework, to address these challenges. InstructRAG includes a graph to organize past instruction paths (sequences of correct actions), an RL-Agent with Reinforcement Learning to expand graph coverage for enlargability, and an ML-Agent with Meta-Learning to improve task generalization for transferability. The two agents are trained end-to-end to optimize overall planning performance. Our experiments on four widely used task planning datasets demonstrate that InstructRAG significantly enhances performance and adapts efficiently to new tasks, achieving up to a 19.2% improvement over the best existing approach.

InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning

The paper titled "InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning" proposes a novel approach to improving task planning capabilities using large language models (LLMs). As advancements in LLMs continue to demonstrate their potential for complex reasoning and planning, this research addresses the limitations of LLMs by introducing a retrieval-augmented generation (RAG) framework that integrates external data sources. Specifically, InstructRAG employs instruction graphs to systematically organize and optimize instruction paths retrieved from past experiences to enhance planning strategies. This approach aims to overcome the restricted knowledge inherent in LLMs when handling complex tasks.

Core Contributions

1. Problem Identification and Challenges: The paper identifies two critical challenges in applying RAG to task planning—enlargability and transferability. Enlargability refers to expanding the scope of instruction graphs by combining instructions into new paths, effectively covering more questions. Transferability involves rapid adaptation to new tasks, emphasizing the importance of extending techniques for diverse applications in practice.

2. Framework Overview: InstructRAG is developed within a multi-agent meta-reinforcement learning framework, incorporating an instruction graph, an RL-Agent, and an ML-Agent. The RL-Agent retrieves paths based on graph traversal, modeling the process as a Markov Decision Process and optimizing it using reinforcement learning. The ML-Agent selects the most relevant path for prompt construction, enhancing task generalization through meta-learning.

3. Instruction Graph Construction: The framework constructs instruction graphs to systematically organize successful instruction paths from task-specific datasets. This graph forms a natural structure for integrating paths and expands its coverage by combining stored instructions, improving the database's capability to address a wider range of questions.

4. End-to-End Optimization: The framework explicitly trains the RL-Agent and ML-Agent to optimize overall task planning performance, leveraging a multi-agent pipeline to facilitate effective collaboration between agents during training, few-shot adaptation, and testing stages.

Experimental Results

The research evaluates InstructRAG on four widely used task planning datasets—HotpotQA, ALFWorld, Webshop, and ScienceWorld—across three LLMs, namely GLM-4, GPT-4o mini, and DeepSeek-V2. Experiments confirm that InstructRAG achieves significant performance improvements over existing methodologies, with notable enhancement in task adaptability. Specifically, it shows up to 19.2% improvement over the best existing approaches on HotpotQA, demonstrating superior task planning capabilities and efficient adaptation to new challenges.

Implications and Future Directions

The practical implications of this research extend beyond the demonstrated task planning capabilities, influencing both theoretical and applied research in AI. InstructRAG's use of instruction graphs provides a robust framework for bridging the gap between diverse task-specific questions and the grounded generation of answers. This advancement highlights the importance of integrating structured external knowledge into LLM operation, transcending the innate limitations of current models. The promising results prompt further exploration into refining and extending these capabilities to a wider array of real-world applications.

Future developments could explore expanding the methodology to integrate more complex datasets globally, fine-tuning multi-agent frameworks, and addressing computational challenges. Additionally, the approach could further leverage the synergy between the RL-Agent and ML-Agent, enhancing collaborative dynamics for continuous improvement in task planning performance.

In conclusion, the paper's contributions provide essential insights into the growing capabilities and potential applications of LLMs in systematic task planning, advancing the field toward more resilient, adaptive, and context-aware AI solutions.