Meta 论文比较前沿智能体的经验学习能力
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.
Meta 新论文让权重冻结的智能体在象棋、围棋、Hex 和 NetHack 中反复改写自己的工具与笔记，再用留存对局表现和每美元学习成本衡量其可塑性。在困难象棋中，Claude Fable 5 的留存对局胜率从 37.5% 升到 73.3%；在象棋、围棋和 Hex 上，GPT-5.6 Sol 每 1000 美元学习成本提升 298 分，Claude Fable 5 为 57 分。作者建议评测自我改进型智能体时跟踪留存学习曲线与成本，并检查智能体是否真正用上了自己保存的内容。
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Meta 这篇论文把智能体的经验学习拆成留存收益与学习成本两个维度，给出了可迁移的评测思路。
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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.