arXiv abstract for Meta's "Agent Plasticity" self-improvement paper
rohanpaul_ai · x · 2026-10-18
This post links to the full arXiv abstract of Agent Plasticity: Measuring Self-Improvement Through Experience (arXiv:2610.08902) by Meta Superintelligence Labs, UC Berkeley, Princeton and others (authors include Jason Weston, Rob Fergus, Anirudh Goyal, Sanjeev Arora).
The paper argues evaluating self-improving agents requires answering three questions: whether performance improves and generalizes beyond learning interactions, how efficiently new capabilities are acquired, and where the self-improvement loop breaks down. It introduces agent plasticity — the efficiency of converting experience into held-out gains — in controlled settings where agents amortize experience into reusable artifacts with frozen weights. Frontier models show sharply divergent improvement trajectories given comparable learning opportunities; the eventual best performer is not necessarily the most efficient learner, and in-distribution gains often transfer only partially out-of-distribution.
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