别被百万token的噱头骗了！这位工程师用GPT-5.5的数据告诉你，上下文越长准确率反而暴跌，真正的解法是持续学习和真实环境训练。
Prime Intellect工程师指出，GPT-5.5在256k token上下文时检索准确率达80%，但扩展到1M token时准确率骤降至36%。工程师称这种现象为“context rot”（上下文腐烂），即模型虽能接受长上下文，但无法有效推理。他建议通过持续学习、基于自身轨迹训练和真实环境来改进，而非单纯扩大上下文窗口。该观点引发对长上下文实用性的讨论。
源：https://t.co/lWQAsxIMGY
源： x.com/0xCarnagee/sta… Carnage @0xCarnagee Prime Intellect engineer: "everyone's bragging about a million-token context. here's what they don't tell you. at 256k tokens GPT-5.5 scores 80% on retrieval. push it to a million and it drops to 36%. the model accepts the context, it just can't reason across it. people call it context rot." in a 20-minute talk he explains why bigger context windows won't save your agents. continual learning + training on your own traces + real environments - that's the fix. Watch the talk, then save! Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 0 👀 494 ⚡