Memp: Exploring Agent Procedural Memory
Memp enhances agent procedural memory by distilling past trajectories, showing a 50% success rate improvement.
Key Findings
Methodology
The Memp framework distills past agent trajectories into fine-grained, step-by-step instructions and higher-level script-like abstractions. It explores strategies for building, retrieving, and updating procedural memory, coupled with a dynamic regimen to ensure the memory repository evolves with new experiences.
Key Results
- Empirical evaluation on TravelPlanner and ALFWorld shows that as the memory repository is refined, agents achieve steadily higher success rates and efficiency, with a 50% improvement in success rate.
- Procedural memory built from a stronger model retains its value when migrated to a weaker model, showing significant performance gains.
- By eliminating fruitless exploration, the number of steps is reduced by 50%, demonstrating the effectiveness of procedural memory.
Significance
This research addresses the brittle procedural memory in LLM-based agents by introducing a learnable, updatable procedural memory, significantly enhancing agent success rates and efficiency across diverse tasks.
Technical Contribution
Memp provides new strategies for constructing, retrieving, and updating procedural memory, particularly introducing a dynamic update mechanism that allows agents to efficiently adapt and optimize their knowledge base in dynamic environments.
Novelty
Memp is the first to systematically explore procedural memory construction and update strategies, offering a more refined memory management mechanism compared to existing memory-augmented frameworks.
Limitations
- In highly dynamic environments, the update of procedural memory may lag, affecting performance.
- Requires substantial initial data to construct effective procedural memory.
Future Work
Future work could explore more efficient memory update strategies and test Memp's applicability in more diverse task environments.
AI Executive Summary
Large Language Model (LLM) agents excel at diverse tasks but suffer from brittle procedural memory, limiting their performance in complex tasks. Existing solutions often rely on manually engineered or static parameters, which struggle to adapt to dynamic environments. The Memp framework addresses this by distilling past agent trajectories into fine-grained, step-by-step instructions and higher-level script-like abstractions, providing a learnable, updatable procedural memory solution.
The core of the Memp framework lies in its strategies for building, retrieving, and updating procedural memory. With a dynamic update mechanism, Memp ensures the memory repository evolves with new experiences, enhancing agent success rates and efficiency in complex tasks. Experiments show a 50% improvement in success rates and a 50% reduction in steps as the memory repository is refined.
While Memp makes significant advances in procedural memory management, its update mechanism needs optimization in highly dynamic environments. Future research could explore more efficient memory update strategies and test Memp's applicability in more diverse task environments.
Deep Analysis
Background
Large Language Models (LLMs) excel at diverse tasks but suffer from brittle procedural memory, limiting their performance in complex tasks. Existing memory-augmented frameworks like LangGraph and AutoGPT provide coarse abstractions but lack optimization in procedural memory lifecycle operations.
Core Problem
The core problem is how to provide agents with learnable, updatable procedural memory to enhance success rates and efficiency in complex tasks. This requires overcoming the limitations of manually engineered memory and the difficulty of updating static parameters.
Innovation
The Memp framework distills past agent trajectories into fine-grained, step-by-step instructions and higher-level script-like abstractions. It explores strategies for building, retrieving, and updating procedural memory, coupled with a dynamic regimen to ensure the memory repository evolves with new experiences.
Methodology
- �� Build: Construct procedural memory using historical trajectories or explicit guidelines.
- �� Retrieve: Employ query-vector matching and keyword-vector matching strategies.
- �� Update: Introduce ordinary addition, validation filtering, reflection, and dynamic discarding strategies.
Experiments
Experiments were conducted on the TravelPlanner and ALFWorld benchmark datasets, validating Memp's effectiveness in long-horizon housework and information-seeking tasks. The experiments utilized strong models like GPT-4o and Claude.
Results
Results show that as the memory repository is refined, agent success rates and efficiency significantly improve. Procedural memory built from a stronger model retains its value when migrated to a weaker model, showing significant performance gains.
Applications
Memp can be applied in fields requiring complex task planning and execution, such as automated household management and complex information retrieval. Its dynamic update mechanism ensures efficiency in changing environments.
Limitations & Outlook
In highly dynamic environments, the update of procedural memory may lag, affecting performance. Additionally, constructing effective procedural memory requires substantial initial data.
Plain Language Accessible to non-experts
Imagine a kitchen where a chef needs to remember the steps for each dish. Memp acts like a smart assistant, helping the chef record and update each recipe's steps. Every time the chef learns a new technique or discovers a better method, the assistant updates the recipe. This is like what Memp does in agents: recording past experiences to optimize future task execution.
ELI14 Explained like you're 14
Imagine you're playing a complex game where you have to remember a lot of steps to pass each level. Memp is like a super memory assistant that helps you record the strategies for each level. Next time you face a similar level, you can pass it faster and easier! Isn't that cool?
Glossary
Large Language Model (LLM)
A large neural network model capable of processing and generating natural language.
Used as the foundational model for agents to perform complex tasks.
Procedural Memory
A type of long-term memory responsible for knowing how to perform tasks and skills.
Core to the Memp framework, enhancing agent task execution capabilities.
Dynamic Update Mechanism
A strategy that continuously updates, corrects, and deprecates contents.
Ensures the procedural memory repository evolves with new experiences.
TravelPlanner
A benchmark dataset designed to evaluate agent capabilities in complex planning tasks.
Used to validate Memp's effectiveness.
ALFWorld
A dataset comprising household tasks for long-horizon tasks.
Used to test agent procedural memory capabilities.
Open Questions Unanswered questions from this research
- 1 How to optimize procedural memory updates in highly dynamic environments?
- 2 What is Memp's applicability in more diverse task environments?
Applications
Immediate Applications
Automated Household Management
With the Memp framework, household robots can complete complex tasks more efficiently, reducing human intervention.
Long-term Vision
Intelligent Information Retrieval
Memp can be used in complex information retrieval systems to improve search efficiency and accuracy.
Abstract
Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters. In this work, we investigate strategies to endow agents with a learnable, updatable, and lifelong procedural memory. We propose Memp that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions, and explore the impact of different strategies for Build, Retrieval, and Update of procedural memory. Coupled with a dynamic regimen that continuously updates, corrects, and deprecates its contents, this repository evolves in lockstep with new experience. Empirical evaluation on TravelPlanner and ALFWorld shows that as the memory repository is refined, agents achieve steadily higher success rates and greater efficiency on analogous tasks. Moreover, procedural memory built from a stronger model retains its value: migrating the procedural memory to a weaker model can also yield substantial performance gains. Code is available at https://github.com/zjunlp/MemP.