图灵奖得主萨顿说合成数据是扩展大模型的“大错误”，他认为世界太复杂，应该让智能体从真实经验中学习。
图灵奖得主理查德·萨顿称合成数据是扩展大语言模型的“大错误”。他认为世界是无限复杂的，任何模拟都只是“微观的”。人类专业知识会成为瓶颈，阻碍真正的扩展。萨顿的替代方案是让智能体从自身经验中持续学习，而不是依赖冻结的模型。
KI-Pioneer Sutton calls synthetic data a "big mistake" in the face of an infinitely complex world
Turing Award winner Richard Sutton calls synthetic data a "big mistake" for scaling large language models. The world is infinitely complex, and any simulation of it is "microscopic," with human expertise acting as a bottleneck that blocks real scaling. Sutton's alternative is agents that learn continually from their own experience instead of relying on frozen models. The article KI-Pioneer Sutton calls synthetic data a "big mistake" in the face of an infinitely complex world appeared first on The Decoder .