重新思考 Agent 的泛化能力：MiniMax M2 探討「我們究竟在對齊什麼？」
Original: Aligning to What? Rethinking Agent Generalization in MiniMax M2
This article, published on the Hugging Face Blog, explores one of the most cutting-edge topics in the AI field today: **the challenges of…
本文探討 MiniMax 在 Agent 領域的最新研究思考。傳統 LLM 對齊（如 RLHF）偏重人類對話喜好，但對需要操作工具、適應動態環境的 AI Agent 而言，這種方式無法提升其泛化能力。MiniMax M2 提出重新定義 Agent 的對齊目標，應從「對齊人類偏好」轉向「對齊環境反饋與任務成功率」，以解決 Agent 在面對未知環境時的泛化瓶頸。
This article, published on the Hugging Face Blog, explores one of the most cutting-edge topics in the AI field today: **the challenges of alignment and generalization for AI Agents**, and introduces MiniMax's new thinking in its M2 research.
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