Meta 论文比较前沿智能体从经验中学习的效率差异
Meta 新论文指出前沿智能体从经验中学习的方式差异很大，表现最好的并非学习效率最高的。研究冻结模型权重，让智能体在国际象棋、围棋、Hex 和 NetHack 中反复改写自己的工具与笔记，并以留出局的每美元学习成本增益定义可塑性。
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：rohanpaul_ai · x.com