arXiv:2605.17774v2 Announce Type: replace Abstract: Large language models are increasingly used as planning components in agentic systems, but current tool-use pipelines often require full tool schemas to be included in every prompt, creating substantial token overhead and limiting the practicality of smaller models. This paper investigates whether tool-use knowledge can be internalized into small language models through parameter-efficient fine-tuning, enabling structured planning without explicit tool descriptions at inference time. Using AssetOpsBench as the primary benchmark, we fine-tune
This paper addresses a current limitation in LLM agentic systems, where token overhead for tool schemas hinders the practicality of smaller models, making 'now' an opportune time for efficiency improvements.
This research suggests a path for small language models to achieve sophisticated tool-use capabilities with reduced computational overhead, potentially democratizing advanced AI agent deployment.
Small language models could become significantly more capable in agentic systems without explicit tool descriptions, increasing their utility and reducing operational costs for tool-use applications.
- · Developers of small language models
- · Companies seeking cost-effective AI agents
- · Edge AI computing
- · AI agent platform providers
- · Providers of large, computationally intensive LLMs (for specific agentic tasks)
- · Companies reliant on large token windows for tool integration
Increased practical deployment of AI agents utilizing smaller, more efficient LLMs.
A shift in demand towards highly optimized, parameter-efficient fine-tuning methods for specialized AI agent tasks.
Potential for a new wave of localized or specialized AI agents running on less powerful hardware, expanding the scope of AI application.
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