AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes (2506.14728v1)

Abstract: While knowledge distillation has become a mature field for compressing LLMs into smaller ones by aligning their outputs or internal representations, the distillation of LLM-based agents, which involve planning, memory, and tool use, remains relatively underexplored. Existing agent distillation methods typically replay full teacher trajectories or imitate step-by-step teacher tool usage, but they often struggle to train student agents to dynamically plan and act in novel environments. We propose AgentDistill, a novel, training-free agent distillation framework that enables efficient and scalable knowledge transfer via direct reuse of Model-Context-Protocols (MCPs), which are structured and reusable task-solving modules autonomously generated by teacher agents. The reuse of these distilled MCPs enables student agents to generalize their capabilities across domains and solve new problems with minimal supervision or human intervention. Experiments on biomedical and mathematical benchmarks demonstrate that our distilled student agents, built on small LLMs, can achieve performance comparable to advanced systems using large LLMs such as OctoTools (GPT-4o), highlighting the effectiveness of our framework in building scalable and cost-efficient intelligent agents.

• The paper introduces AgentDistill, a novel framework that distills task-solving capabilities through modular and reusable MCPs without fine-tuning.
• It replaces gradient-based training with MCP abstraction, clustering, and consolidation, streamlining knowledge transfer and reducing computational costs.
• Experimental results show student agents nearing GPT-4 performance on biomedical and arithmetic tasks, underlining its efficiency and scalability.

AgentDistill: A Training-Free Agent Distillation Framework

The paper introduces AgentDistill, a novel framework aimed at distilling task-solving capabilities from LLM-based agents into smaller counterparts. This framework is centered around the concept of Model-Context-Protocols (MCPs), which are structured and reusable modules generated by teacher agents. Unlike traditional distillation methods that require extensive training, AgentDistill eliminates the need for trajectory replay or fine-tuning, allowing student agents to inherit advanced capabilities directly through MCP integration.

Overview of Key Concepts

• Model-Context-Protocols (MCPs): MCPs serve as task-solving modules generated by teacher agents. These protocols encapsulate the problem-solving capabilities and are designed to be modular, reusable, and generalizable across different task domains.
• Training-Free Distillation: The core innovation of AgentDistill lies in its ability to transfer knowledge without altering the student agent through gradient updates. Instead, the framework involves MCP abstraction, clustering, and consolidation into an MCP-Box available to the student agent at inference.
• Efficiency and Scalability: By leveraging MCPs, student agents equipped with small LLMs can achieve performance levels comparable to agents employing large-scale models like GPT-4o.

Numerical Results and Bold Claims

The paper reports robust experimental results across biomedical and mathematical benchmarks, underscoring the efficacy of AgentDistill. Student agents enriched with MCPs exhibit substantial improvements, closing the performance gap with more advanced systems. For example, notable gains are observed in biomedically focused visual question answering tasks and arithmetic reasoning tests. These results are presented in comparative analyses across different frameworks, including OctoTools and tool-augmented systems.

Implications for Research and Practice

AgentDistill introduces a paradigm shift in agent development, emphasizing training-free frameworks that improve efficiency by reducing computational demands. This approach has significant implications for scaling AI applications across domains that require complex reasoning and tool interaction.

• Practical Implications: AgentDistill is applicable in various domains like finance, chemistry, and clinical practice, suggesting its potential for wide-scale adoption in specialized AI systems. The ability to inherit sophisticated capabilities without additional training dramatically reduces inference cost and complexity.
• Theoretical Implications: This framework challenges the prevailing notions of agent skill transfer, highlighting the potential of using MCPs to achieve generalization and adaptability in smaller models that were previously reliant on extensive teacher-student training pipelines.

Future Directions

The paper paves the way for further research into MCP-based distillation strategies, encouraging exploration into:

• Expanding MCP Protocol Libraries: Creating comprehensive repositories that support a wider array of tasks and domain-specific challenges.
• Refining MCP Abstraction Techniques: Improving the abstraction process to facilitate more efficient protocol development and deployment.
• Integration with Emerging AI Frameworks: Adapting MCP-based tools within new AI models to explore synergies beyond traditional LLM constraints.

In conclusion, AgentDistill represents a significant advancement in distillation practices, advocating for streamlined, cost-efficient methods that hold promise for both practical applications and theoretical advancements in AI. By leveraging the inherent modulability of MCPs, this framework sets a new standard for knowledge transfer in agent systems.