Synthesizing tool-call training data by executing the tools first lets a 9B model nearly match a 27B one

arXiv 2609.05395·medium signalXBlueskyLinkedInCopy link

KOPA-Bench and EDGE (arXiv 2609.05395, v1 2026-09-04) target multi-step tool chaining against live government APIs, a setting where data-sovereignty rules force on-premise open-source agents and where open models consistently underperform. EDGE builds a graph of how each tool's output can feed another tool's input, keeps only the links that actually succeed when called against the live APIs, and traverses those verified links to synthesize executable multi-step trajectories. Fine-tuned with GRPO on that data, their 9B model nearly matches the untuned 27B model from the same family, improving on both the 145-task KOPA-Bench and on BFCL. The reusable idea is that execution-grounded edge pruning is what makes synthetic tool-call data useful; generating plausible chains without calling the APIs is what produces the usual garbage.