Small LLMs internalize tool knowledge via QLoRA fine-tuning

Researchers have developed a method to internalize tool knowledge into small language models using QLoRA fine-tuning, reducing the need for explicit tool schemas in prompts. By training models like Gemma 4 E4B and Qwen3-4B on tool-use examples, they achieved better planning scores than a baseline that received full tool descriptions. This approach significantly cuts down on input length and inference overhead while maintaining or improving tool-planning quality, though it may impact general knowledge retention. AI

影响 Enables more efficient use of smaller models in agentic systems by reducing prompt token overhead.

排序理由 The cluster contains an academic paper detailing a new fine-tuning method for small language models. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

• AssetOpsBench
• Gemma 4 E4B
• QLoRA
• Qwen3-4B

• 论文
• 模型发布

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