Meta 论文提出 Agent Plasticity，衡量智能体从经验中学习的效率差异
Meta 论文提出 Agent Plasticity 衡量智能体从经验中学习的效率差异
Meta 论文提出 Agent Plasticity 指标，用"每美元学习成本带来的留出集增益"衡量智能体从经验中学习的效率。实验冻结模型权重，让智能体在象棋、围棋、Hex 和 NetHack 中反复重写自己的工具与笔记，再在留出对局上评分。
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.
来源：Rohan Paul · x.com