Our latest research, TTT-E2E, marks a new era for LLM memory. Now, models can continue training du...
TL;DR · AI 摘要
斯坦福AI实验室提出TTT-E2E方法，允许大模型在部署时利用上下文持续训练并更新权重。
核心要点
- TTT-E2E使LLM能在推理时用上下文数据持续训练
- 该方法旨在解决大模型长期记忆难题
- 研究由斯坦福、NVIDIA和Astera Institute合作完成
Now, models can continue training during deployment, using context as training data to update their weights and learn from massive amounts of experience.
With @NVIDIAAI and @AsteraInstitute" / X
Stanford AI Lab on X: "Our latest research, TTT-E2E, marks a new era for LLM memory. Now, models can continue training during deployment, using context as training data to update their weights and learn from massive amounts of experience. With @NVIDIAAI and @AsteraInstitute" / X
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Our latest research, TTT-E2E, marks a new era for LLM memory. Now, models can continue training during deployment, using context as training data to update their weights and learn from massive amounts of experience. With
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Quote
Karan Dalal
@karansdalal
·
Jan 12
LLM memory is considered one of the hardest problems in AI. All we have today are endless hacks and workarounds. But the root solution has always been right in front of us. Next-token prediction is already an effective compressor. We don’t need a radical new architecture. The
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