Verified65% confidenceFactExact time
The closer an event is to the cutoff date, the smaller its share in the training corpus, and the less clearly the model has mastered it.
3
Sources
65%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
Related Entities
Related Claims
Unverified'Slowing down' lacks an actionable definition, and no consensus has been reached on which metrics to slow (model parameter scale, training compute investment, or capability release schedules).79% similarUnverifiedIf samples exceeding 32K tokens rarely appear in a model's pre-training corpus, the model's performance in that range tends to be unstable even when the context window is extended through positional encoding.77% similarUnverifiedFew-step generation approaches involve a quality-speed trade-off: with too few steps, the model struggles to correct early errors, which can lead to blurry details or unnatural motion.77% similarUnverifiedDuring cross-domain fine-tuning, an excessively large learning rate will destroy pre-trained weights, while an excessively small one will result in almost no learning, requiring a careful trade-off.75% similarUnverifiedContext confusion produces narrow misalignment, where misalignment is concentrated in specific domains semantically close to the training data, rather than a broad degradation of the model's overall behavior.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/913207API
curl https://kongchang.com/api/v1/knowledge/claims/913207MCP
get_claim(id=913207)