Meta researchers propose agent plasticity to measure self-improvement efficiency
AIResearchers from UC Berkeley, Meta Superintelligence Labs, and other institutions introduce agent plasticity, the gain on held-out tasks per dollar of learning cost, with model weights frozen. The paper reports that in chess, Go, and Hex, Claude Fable 5 reaches the highest final score while GPT-5.6 Sol gains the most per dollar, and in NetHack only Claude Opus 5.5 improves significantly.









