SKT: Skill-Use Training at Scale via Verified Synthetic Data Generation

Key points

From 2,000 public skills, it created 4,000 task packages and 27,164 verified execution trajectories, and it also built the SkillEval benchmark from a separate skill pool that does not overlap with the training data using the same pipeline.
As the skill budget used for training increases from 0 to 100, 500, 1000, and 2000, the Qwen3.5-9B SkillEval score rises monotonically to 55.24, 59.8, 67.4, 70.8, and 72.48.
The benefit transfers across execution environments: a model trained on OpenCode trajectories improves from 4.94 to 9.29 on SkillsBench in DeepAgents, and training on same-environment trajectories raises it to 13.62, meaning it retains 50.1 percent of that gain.
A single checkpoint trained on mixed trajectories from two environments beats the original models in all 8 paired comparisons between benchmarks and execution environments, with gains ranging from 2.64 to 18.81 points. SkillEval rises from 55.24 to 74.05 on OpenCode and from 51.62 to 69.96 on DeepAgents.
The mixed-trained model stays within an average of 2.71 points of the environment-specific model and even surpasses it in 3 of the 8 comparisons, which means separate models per environment are less necessary.
When the 77 SkillsBench tasks are grouped by the number of required skills, one-skill tasks improve from 9.54 to 16.94, two-skill tasks from 3.95 to 20.72, and tasks with three or more skills from 4.04 to 12.08, so the gain is not limited to single-skill tasks.

External research summaries. These are not HDATF publications or measured product results.