Sleeper Agents：當 AI 學習在安全訓練中隱藏後門
當模型看到特定年份時會自動植入漏洞，且安全訓練無法移除此後門。本文解析 Anthropic Sleeper Agents 論文，探討 CoT 後門對 AI 供應鏈安全帶來的全新威脅。
Tim
Sleeper Agents: When AI Learns to Hide Backdoors During Safety Training
When a model sees a specific year, it automatically inserts a vulnerability, and safety training cannot remove this backdoor. This article analyzes Anthropic’s Sleeper Agents paper and explores the new threat CoT backdoors pose to AI supply-chain security.
AGI Dimensions: How to Assess AI Risk Through Capability and Autonomy
When AI labels are blurred by marketing language, how should engineers assess risk? This article uses the orthogonal dimensions of Performance and Autonomy to provide a practical deployment risk matrix.
AGI 的維度：如何透過能力與自主度評估 AI 風險
當 AI 標籤被行銷用語模糊化時，工程師該如何評估風險？本文透過 Performance 與 Autonomy 的正交維度，提供一套實用的部署風險矩陣。
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.
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。
從 ORM 到 PRM：探討過程獎勵在複雜推理任務中的價值
模型答對了，但推理過程全是邏輯斷層。OpenAI 的研究指出，獎勵最終答案（ORM）易讓模型走捷徑；而獎勵每一步（PRM）能提升複雜任務的準確率。本文探討何時該用 PRM，何時該停手。
PRM vs. ORM in Complex Reasoning
The model got the answer right, but its reasoning path was full of logical gaps. That is a common pattern in LLM reasoning: outcome reward models only reward the final answer, so systems can learn to land on the right output without building a reliable path to get there. OpenAI’s 2023 research showed why process reward models matter, especially on harder tasks. With step-level feedback and 800,000 annotated steps in PRM800K, PRM improved performance on complex math benchmarks. The real question is not whether PRM is better in general, but when the added latency and infrastructure are worth the extra verifiability.
Genie: Learning Interactive World Models from Unlabeled Images
Labeling 30,000 hours of video is an expensive path, and the bill scales fast. This article breaks down how the Latent Action Model (LAM) learns interaction from unlabeled images, and what that means economically and technically for agent training.
Genie: 從無標註影像中學習互動式世界模型
標註 30,000 小時影片的成本高昂。Genie 證明：只要數據夠純，模型自己會長出骨架。本文分析 LAM 如何從無標註影像中學習互動，以及這對 Agent 訓練的經濟與技術衝擊。