Brilliant work on using many harness configurations to scale agentic RL. Many approaches use mainly one fixed harness, which specializes the model but doesn't generalize well. This work uses many harness configurations as a scaling dimension. But the big question is whether it can generalize to harness configurations never seen in training. They pick a compact training subset with balanced factorial coverage, and vary one part of the harness at a time on the same task. Across benchmarks,…