<ul><li>ToolACE-R is a novel method for tool learning with adaptive self-refinement.</li><li>Current tool learning approaches mainly focus on data synthesis for fine-tuning models but overlook fully stimulating model potential.</li><li>ToolACE-R incorporates an iterative training procedure that progressively incorporates more training samples based on the model's evolving capabilities.</li><li>Experimental results show that ToolACE-R achieves competitive performance and can further improve through adaptive self-refinement.</li></ul>

ToolACE-R: Tool Learning with Adaptive Self-Refinement

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