François Chollet 提出疑问：能力边界是否主要取决于数学与代码
Keras 之父 Chollet 抛了个有意思的问题：RLVR 只能拉高数学和代码，其他领域能不能跟着涨还不确定，这直接决定接下来模型能走多远。
François Chollet 在 X 上提出一个假设：模型能力的不均衡前沿主要由数学和代码构成，RLVR 可以在这两个领域无限推进，其他领域则因受制于人类生成数据而趋于平台期。他指出非可验证领域的表现仍在稳步提升，但速度远慢于数学和代码。关键问题在于这种提升是来自 RLVR 驱动的通用智能上升，还是仅靠持续注入海量人类数据维持。
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
