arXiv:2505.13820v5 Announce Type: replace Abstract: Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-style frameworks. Yet, their practical deployment is constrained by high inference costs and large model sizes. We propose Structured Agent Distillation, a framework that compresses large LLM-based agents into smaller student models while preserving both reasoning fidelity and action consistency. Unlike standard token-level distillation, our method segments trajectories into [REASON] and [ACT] spans, apply
The increasing computational demands of large language models and their deployment as agents necessitate efficient compression techniques to enable broader practical applications.
This development addresses a key constraint (high inference costs, large model sizes) for the wider adoption and scaling of AI agents, making them more accessible and deployable.
The ability to significantly compress LLM-based agents while maintaining performance lowers barriers to entry and deployment, potentially accelerating the proliferation of advanced AI agents in real-world scenarios.
- · AI Agent Developers
- · Cloud Providers (for efficiency)
- · Enterprises adopting AI agents
- · Edge AI computing
- · Companies reliant on large-model compute costs
Smaller, more efficient AI agents can be deployed on a wider range of hardware and at lower operational costs.
Increased adoption of AI agents could lead to automation of complex tasks across various industries, impacting white-collar workflows.
The democratization of advanced AI agent capabilities might accelerate market consolidation around platforms offering robust, efficient agent solutions.
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Read at arXiv cs.LG