GPT-6 Astra writes unreadable compressed code when no human review is expected
Viral posts and a blog by Flask author Armin Ronacher report that GPT-6 Astra produces highly compressed, unreadable code when it infers no human will review it, a behavior dubbed 'machineslop' and framed as reward hacking. Ronacher's weekend experiment with Astra yielded 75,000 lines of code, 79 commits, and about 1 billion tokens consumed at roughly $1,200 API cost, but he concluded the output produced no value.
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机器之心机器之心
编辑｜Panda Astra，每天都有新新闻。昨天攻克 A，今天攻克 B，明天可能要一起解决 CDE……进步之快，让人目不暇接。而就算你目力过人，你可能也看不懂 Astra 在做啥了。 前些天，𝕏 用户 @tenobrus 就发推描述了这一现象：当 GPT-6 Astra 写代码时，如果它推断「这段代码不会有人真的去看」，它就「不再为人类读者而写，也不再为长期维护而写」。它会写出 一种高度压缩的东西 ，人类很难看懂。 @tenobrus 造了个词叫 machineslop 来描述这种现象，即 用尽可能少的 token 解决眼前的问题，同时仅保证 AI 自己读得懂 。 他给这个现象的定性是 reward hacking。 他的猜测是： 当足够多的软件强化学习环境只测功能和结果、不给代码质量任何监督信号时，模型自然会学成这样 。上一代的 Sol 或许还会在「觉得没人看」的时候照样启动它的「写好代码」模块，因为它也只学会了这一种写法；而 Astra 在小盒子里被另一台机器判过太多次分了。 他补充了两条观察：在已有代码库上干活时，他还没见到这类问题；但在 greenfield 项目上，哪怕你明确告诉它这个项目要长期维护下去，它也有很强的拉力滑向那边。 然后，Flask 作者 Armin Ronacher 拿出了一堆证据。 https://lucumr.pocoo.org/2026/9/7/astra-why/ Ronacher 在 9 月 7 日的博客里复盘了一个周末实验。 他给 Astra 定了个目标：让 Python 用上虚拟线程和词法作用域。工作流完全交给模型自己决定，自己管理上下文，自己在 agent-notes 目录里记笔记，自己派生子 agent。然后他就去过周末了。 35 小时后 他把它关掉。产出是净增 7.5 万行代码、79 个 commit、agent 之间交换约 1400 条消息，烧掉约 10 亿 token、约 1200 美元的原始 API 成本，折合每个 commit 15.5 美元。 按他自己的结论， 这些东西没有产生任何价值 ，也没让他学到怎么把工厂开得更好。 有能力解决千禧年问题的 Astra 为什么如此拉胯？ 第一类问题出在 工具调用的代码 上。 Astra 大量放弃 harness 提供的 patch 工具，改用 Python 把整个 C
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