Unverified50% confidenceFactExact time
SkillScaffold's ablation experiments verified that the utility-based selection and abandonment mechanism is one of the key sources of performance improvement.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/14/2026
First Seen
Valid until: 1/12/2027
Sources
Related Entities
Related Claims
UnverifiedSkillScaffold reuses training trajectories whose results have already been verified and uses outcome-aware likelihood comparison to estimate skill utility, without relying at all on candidate-specific environment interaction.80% similarUnverifiedThe advantage of the specialist route is that a narrower task scope makes evaluation, error correction, and optimization more tractable, resulting in higher output stability.73% similarUnverifiedExamining the model's action sequences during training one by one can determine whether high scores result from genuine capability improvement or from exploiting loopholes in the reward mechanism.72% similarUnverifiedTurning agent skill optimization into an iterative, evaluable engineering workflow helps improve reproducibility and measurability, at lower cost, with faster iteration, and without relying on retraining the base model.72% similarUnverifiedRecommendations for practicing Skills include identifying high-frequency reuse scenarios, clarifying trigger conditions, establishing a rapid testing feedback loop, and maintaining single responsibility.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1028035API
curl https://kongchang.com/api/v1/knowledge/claims/1028035MCP
get_claim(id=1028035)