New Meta paper shows that frontier agents differ widely in how they learn from experience, and the best performer is not the most efficient learner. Most agent tests measure what a model can do once. They ignore whether it gets better by building tools, notes and skills from past attempts, and at what cost. They froze model weights and let agents play chess, Go, Hex and NetHack, rewriting their own tools and notes after each round. They then scored held-out games and defined plasticity as held-out gain per dollar spent learning. In hard chess, Claude Fable 5 rose from 37.5% to 73.3% on held-out games. Across chess, Go and Hex, GPT-5.6 Sol gained 298 points per $1,000 of learning cost, versus 57 for Claude Fable 5. When you evaluate self-improving agents, track held-out learning curves and cost, and check whether the agent actually uses what it saved.