Knowledge Distillation for Tool-Calling Agents: A Technical Walkthrough
By NeoSmith AI Research Team. March 12, 2026. 16 min read.
Tool calling is the hardest capability to distill. It requires structured output fidelity, schema awareness, and multi-step planning. This walkthrough covers schema-aware tokenization, tool call trace mining, GDPO with tool-specific rewards, and verification gate training.
Key Takeaways
- Distilled SLMs achieve 98.8% tool call schema validity vs 94.2% for frontier LLMs
- GDPO with tool-specific rewards prevents over-optimization for any single dimension
- Verification gate training creates hard constraints against invalid tool calls