用概率模型融合语言与经验，让AI和人更快安全地学习复杂任务。
Language and Experience: A Computational Model of Social Learning in Complex Tasks
- 将语言模型转为概率模型，联合推理语言与感官信息。
- 在10个游戏中加速学习，减少试错风险，提升关键发现速度。
- 支持跨代知识传承，实现人机协同学习，适合协作系统研究者。
人类通过结合他人语言指导与自身经验来快速安全地学习新环境中的复杂任务。我们提出一种计算框架，将社会学习建模为基于传感运动数据和语言信息的结构化可执行世界模型的联合概率推断。通过将预训练语言模型转化为人类信念条件下分享建议的概率模型，使智能体既能生成建议，也能在贝叶斯推断中将语言输入作为证据。在10个视频游戏中的行为实验与模拟显示，语言指导能引导探索、减少高风险互动，并加快关键发现速度，既适用于人类也适用于模型。进一步通过迭代学习实验探究知识跨代积累，验证了人机间成功知识迁移，揭示了结构化、语言兼容表征对人机协作学习的关键作用。
原文摘要 · Abstract (English)
The ability to combine linguistic guidance from others with direct experience is central to human development, enabling safe and rapid learning in new environments. How do people integrate these two sources of knowledge, and how might AI systems? We present a computational framework that models social learning as joint probabilistic inference over structured, executable world models given sensorimotor and linguistic data. We make this possible by turning a pretrained language model into a probabilistic model of how humans share advice conditioned on their beliefs, allowing our agents both to generate advice for others and to interpret linguistic input as evidence during Bayesian inference. Using behavioral experiments and simulations across 10 video games, we show how linguistic guidance can shape exploration and accelerate learning by reducing risky interactions and speeding up key discoveries in both humans and models. We further explore how knowledge can accumulate across generations through iterated learning experiments and demonstrate successful knowledge transfer between humans and models -- revealing how structured, language-compatible representations might enable human-machine collaborative learning.
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