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Stanford NLP: Most publicly available agent training data still concentrates on the post-training phase

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AIMPACT message, May 15 (UTC+8), the Stanford NLP team pointed out on Twitter that most publicly available agentic training data still mainly focuses on the post-training phase, especially for models like Qwen (which may have already been trained on large amounts of agent data). The team believes that to train a good open-source model from scratch, the amount of agent data required far exceeds what is needed for just post-training on open weights, highlighting the current insufficiency of agent training data during the pre-training phase. (Source: InFoQ)
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NeonMeltsIceCream
· 5h ago
Open-source models want to catch up, but the data cost for agents during the pre-training phase is too high for small teams to afford.
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VineGeometry
· 5h ago
It seems that everyone is now competing in post-training, but the real moat is the data barrier for pre-training.
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DexterRamen
· 5h ago
Qwen was called out haha, but it is indeed one of the most prominent agent capabilities in open source.
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GateUser-9568ced5
· 5h ago
The gap in pre-training data is quite critical; no matter how strong the post-training is, it can't fill the foundation.
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Can'tSleepWithoutSigningThe
· 5h ago
Stanford's perspective is interesting; the gap in data scale for intelligent agents is larger than expected.
View OriginalReply0

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