開源模型陷入「永久追趕」：開源與閉源的差距、蒸餾、創新週期與開源的勝算
Original: Open models in perpetual catch-up
This article by Nathan Lambert takes a deep dive into the tangled competitive dynamics between open-source and closed-source AI models…
本文探討開源與閉源 AI 模型之間的動態關係。開源模型（如 Llama、DeepSeek）常利用閉源模型的輸出進行「蒸餾」來快速追趕，但這也讓它們始終落後一步。儘管如此，開源模型憑藉著低成本、高客製化與強大的開發者生態，在實用普及度上依然能取得勝利。然而，要打破這種「永久追趕」的狀態，開源社群仍需在基礎架構創新與自主強化學習（RL）上取得突破。
This article by Nathan Lambert takes a deep dive into the tangled competitive dynamics between open-source and closed-source AI models. Lambert argues that despite the remarkable progress open-source models have made over the past year, they are fundamentally trapped in a state of "Perpetual Catch-up."
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