DGCR: Learning from Local Corrections for Super-Tiny Function Calling Models

Reliable function calling makes super-tiny models promising for efficient tool-based automation. However, local errors can invalidate otherwise useful calls, while unsuccessful rollout groups may be discarded without contributing to learning. Dense rewards give partial credit for correct decisions but do not specify how to repair the remaining errors. We introduce Discarded-Group Counterfactual Repair (DGCR) to recover explicit corrective targets from these rollouts. For collector-rejected groups that satisfy a low-reward condition, DGCR uses training references to propose bounded structural edits and verifies their gains under ordinary and fixed call assignments with consistency checks. It then applies gain-weighted supervision to the repaired spans under student-generated prefixes, without assuming that the complete response is correct. On Qwen3-0.6B, DGCR outperforms strong prior methods on both BFCL v3 and ACEBench, suggesting that even discarded rollouts can provide useful learning signals for super-tiny function calling models.