额度重置 · 用量问题修复

我们正在为 Codex 和 ChatGPT Work 的所有付费用户重置用量。 请继续阅读关于 Codex 用量限额的更新。团队一直在通宵工作，梳理了数千份报告并交付修复。 取决于你如何使用 Codex，你的用量应该会比之前多出 10% 到 50%。 我们确实用细梳子仔细梳理了一遍，发现许多长期存在的小问题，以下是我们发现并修复的内容： - 压缩（Compaction）。我们在压缩时保留了旧图片，有时会让上下文大到再次触发压缩。修复后，重度使用图片的用户的用量下降了约 10%。已修复。 - 记忆（Memory）。后台记忆工作进程可能会继承 Stop 钩子，并在钩子不允许其结束的情况下继续运行。这影响了不到 1% 的用户，但长尾情况相当糟糕，我们看到有一个示例线程检查了自己能否停止 15,000 次。已修复。 - 目标（Goals）。在某些情况下，设定的 /goal 完成后仍会越过预期的停止条件继续运行，或者模型会不断重试出错的工具而不停止。我们看到有些示例消耗了每周额度的 15% 到 70%。已修复。 - 自动化（Automations）。某些自定义计划可能会比配置的频率更频繁地运行。已修复。 - 子代理（Subagents）。较小的模型（例如 Luna）有时会在未被明确要求的情况下挑选能力更强的助手。同样，当编排模型未以 /fast 模式运行时，也可能请求子代理以 /fast 运行。Fi

We are reseting usage for all paid users of Codex and ChatGPT Work. Please continue reading for an update on Codex usage limits. The team has been working around the clock, going through thousands of reports and shipping fixes. Depending on how you use Codex, you should see your usage go between 10% and 50% further than before. We really went with a fine comb, with many uncovered small things being longstanding and here is what we found and fixed: - Compaction. We were keeping old images during compaction, sometimes making the context large enough to trigger compaction again. After the fix, usage dropped around 10% for users making heavy use of images. Fixed. - Memory. Background memory workers could inherit Stop hooks and keep running when the hook wouldn’t let them finish. This affected fewer than 1% of users, with the long tail being pretty bad and we saw one example thread check whether it could stop 15,000 times. Fixed. - Goals. In some cases, a set /goal could finish and then keep going past the intended stop condition, or the model would keep retrying broken tools without stopping. We saw examples consume anywhere from 15% to 70% of a weekly allowance. Fixed. - Automations. Some custom schedules could run more frequently than configured. Fixed. - Subagents. Smaller models (e.g. Luna) sometimes picked more capable helpers without being explicitly asked. The same was true where the orchestrating model not running in /fast mode could request sub-agents to run /fast. Fixed. - Computer History. The older implementation could lead to repeatedly summarizing overlapping activity. For some cases we saw it consume up to one fifth of the weekly usage per week. Fixed. - Rolling task summaries. Ordinary turns were triggering extra background requests. These added about 1% to token usage. Small each time, but it adds up. We have disabled this. - MCP. Some tool results could be encoded twice. We also found tool instructions getting cut off and fetched again. Fixed. We’ve also made architectural changes to prevent these from regressing and our teams will get paged if it happens regardless. We are also working on showing you directly in the app where your usage goes so you don’t have to guess. Goes without saying that we’re resetting usage limits and I hope you enjoy a very nice Saturday!