Chollet 指出，80 年代心理学研究曾假设学编程能像"思维体操"一样提升通用推理与规划能力，但基础研究及后续元分析证明这一迁移并不发生：学编程者算法思维与编码能力变强，收益却不迁移到通用认知任务。数学训练同理，只提升数学演绎与结构映射，不提高无关领域的基线学习速率。他认为通用智能是大脑的固有属性，而非可训练的技能。
Humans provide a fun comparison point here.
In the 80s, the "cognitive consequences of programming" was a major psychological research focus. The prevailing hypothesis was that learning formal logic and algorithmic structures through computer programming would act as a form of mental gymnastics that could upgrade general reasoning, planning, and novel problem-solving abilities. But foundational research, followed by subsequent meta-analyses, demonstrated that this does not happen. Students who learn programming become highly proficient at algorithmic thinking and coding, but these gains do not transfer to general cognitive tasks or unrelated problem-solving scenarios. Similarly, intensive mathematical training improves mathematical deduction and structural mapping, but does not increase an individual's baseline rate of skill acquisition in unrelated fields.
General intelligence seems to be a fundamental property of the brain rather than a skill you can train. Practicing in a domain makes you better at the domain but does not make you generally smarter.
What if the jagged frontier is mainly math + code (which you can push arbitrarily far with RLVR), and everything else starts to plateau because it is still bottlenecked by human generated data? Model performance in non-verifiable areas has kept improving steadily, albeit much slower than for math and code. But is that steady improvement a side effect of a higher G (itself driven by RLVR), or only a function of the amount of new human data getting injected into training (which is still continually happening on a massive scale)? A lot of things depend on the answer to this question在 X 查看被引用的帖子
来源：François Chollet · x.com