Unverified50% confidenceFactExact time
Inference-time search does not update the generative model's parameters, so it does not introduce training instability and also makes it easy to switch optimization objectives quickly.
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedTest-time capability transfer needs no model retraining; the strong model builds the scaffolding and the weak model runs it directly, with no weight updates involved at any point.76% similarUnverifiedThe no-information baseline refers to the performance level when the model receives no rule information, usually close to random guessing or relying on training set bias.75% similarVerifiedAlignment training optimizes the model's statistical behavior in known scenarios rather than establishing formally verifiable security boundaries, and may fail when faced with rare instruction combinations or role-playing frameworks outside the training distribution.73% similarUnverifiedThe frequency-aware training schedule adds no model parameters and does not change inference-time computation, making it a purely training-side improvement.73% similarUnverifiedParametric knowledge is fixed in model weights and cannot be updated without retraining once training is complete; non-parametric knowledge is stored in external databases and can be added, deleted, or modified at any time.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1080547API
curl https://kongchang.com/api/v1/knowledge/claims/1080547MCP
get_claim(id=1080547)