當模型自信地輸出錯誤：價值軸線如何編碼生成軌跡
Anthropic Fellows 研究在 Qwen3-8B 中發現一條線性方向，其投影與信心、回溯及程式碼正確性相關。理解這條軸線與 DPO 訓練的交互作用，有助於評估 Eval Awareness 風險與模型行為邊界。
RLHF
When Models Confidently Produce Errors: How the Value Axis Encodes Generation Trajectories
Anthropic Fellows research found a linear direction in Qwen3-8B whose projection is associated with confidence, backtracking, and code correctness. Understanding how this axis interacts with DPO training helps assess Eval Awareness risks and the boundaries of model behavior.
Constitutional AI: 如何透過 AI Feedback 實現自動化安全治理
標註員 A 給 5 分，B 給 1 分。同一個回答。傳統 RLHF 的瓶頸不在成本，而在訊號雜訊比。本文解析 Constitutional AI 如何透過 AI Feedback 建立自動化安全邊界，以及落地時需要評估的成本結構。
Constitutional AI: How AI Feedback Enables Automated Safety Governance
Annotator A gives it a 5, annotator B gives it a 1. The same answer. The bottleneck in traditional RLHF is not cost, but the signal-to-noise ratio. This article breaks down how Constitutional AI uses AI Feedback to build automated safety boundaries, and the cost structure teams need to evaluate when putting it into practice.
RLHF 的代價：InstructGPT 如何靠對齊而非規模，讓 1.3B 打敗 175B 的 GPT-3
175B 的 GPT-3 輸給了 1.3B 的 InstructGPT。這不是參數的勝利，是『聽話』的勝利。但聽話的代價，是你在部分任務上可能付出能力取捨。本文拆解 RLHF 機制與對齊稅，說明如何在可控性與通用性之間做出取捨。
The Cost of RLHF: Why 1.3B Beat 175B
GPT-3 at 175B lost to InstructGPT at 1.3B. This was not a win for parameter count. It was a win for instruction following. But that gain comes with a tradeoff: when you push a model to stay closer to human preference, you may also narrow its performance on some public NLP tasks. This article breaks down how RLHF works through SFT, reward models, and PPO, then looks at the alignment tax and what it means when you have to choose between controllability and broader capability.