Nace AI 发布 Drex 技术报告，介绍开源决策模型如何用 RLAF 训练。作者指出，27B 的 Drex Max 在 Decision Bench v4 上与 GPT-6 Sol 统计持平，且不生成任何 token：它读取上下文和问题，一次前向传播为每个候选选项返回一个概率。
This is a brilliant release from @NaceAI .
It goes after the costly part of AI agents, the thousands of small choices, with a 27B model that only picks and never writes.
- Nace AI’s 27B Drex Max statistically ties GPT-6 Sol on Decision Bench v4 without generating a token.
- It reads a context and question, then returns one probability per supplied option in a single forward pass.
- Its RLAF training rewards verified answers plus a reasoning judge’s probabilities, while a KL penalty limits drift from supervised training.
- Drex Max costs $0.06 per 1K decisions at 183 ms, against $10.17 and about 3.5 seconds for Claude Opus 5.5.
Without chain-of-thought or tools, it answers 21% of sampled HLE questions against 56% for Opus.
How are Decision Models Trained? Today, we are publishing the Drex Technical Report for the developer community! 🚀 Discover details on how the State-of-the-art Open-Source decision models are trained with RLAF. Build more at: https://console.nace.ai/ (try for free) P.S. Drex Max 1.1 and Drex for Finance are closed sourced and designed for enterprise use-cases在 X 查看被引用的帖子
来源：Rohan Paul · x.com