Unverified50% confidenceDecision RuleExact time
The core filtering logic of SGUID is: only when a certain skill continuously produces effective learning signals during training is it retained.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn multi-agent reinforcement learning, when opponents continuously improve, the training signal automatically remains in a difficulty range that is just challenging enough, avoiding the test-cramming phenomenon of fixed datasets.73% similarVerifiedFeature Stores address the issue of feature computation consistency between training time and inference time, known as training-serving skew.72% similarUnverifiedOnly weights that natively support MTP training can activate this feature on the inference side.68% similarUnverifiedApplication-layer jailbreak defenses must simultaneously rely on robustness improvements at the training stage and dynamic monitoring at the inference stage, as reinforcement along a single dimension yields limited results.67% similarUnverifiedOne-off skills training tends to be superficial; what truly creates value is forming a continuous loop of learning and application.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1013302API
curl https://kongchang.com/api/v1/knowledge/claims/1013302MCP
get_claim(id=1013302)