This research investigates the limitations of simply increasing the scale of multimodal environment pools for training agents. The authors demonstrate that effective agent learning requires optimized environment distributions focused on two key dimensions: diversity and difficulty structure. The study proposes new methodologies to move beyond simple environmental scaling to improve multimodal agent training.