一龍馬/AI 情報站讀懂消息背後的脈絡
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François Chollet 提出疑問:不平滑的能力前沿是否主要集中在數學與程式,因可用 RLVR 持續推進,其他領域則受限於人類生成資料而趨緩

中文摘要

他指出非可驗證領域仍穩步進步,只是慢得多,並追問這是更高通用能力的副作用,還是新人類資料持續注入的結果。貼文是開放提問,沒有給出答案或實驗證據。

一龍馬判讀

答案不同,資料策略與訓練路線就會不同,牽涉模型團隊該重押可驗證獎勵,還是持續爭取高品質人類資料。

原文節錄

François Chollet · @fchollet

bottlenecked by human generated data?…

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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

收錄日期
2026-10-08
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2026/10/08 06:30(台北)