Agent Plasticity is proposed to measure how efficiently AI agents improve on held-out environments

Researchers propose Agent Plasticity as held-out performance gain per unit of learning cost, meant to measure how efficiently agents learn from experience without weight updates. In tests across Chess, Go and Hex, they report Claude Fable 5 reached the highest fitted performance while GPT-5.6 Sol learned about five times more efficiently. They also found that frequently reusing learned artifacts did not guarantee improvement.