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SkillGate addresses selector credit starvation in AI agent skill selection by using separate credit channels for execution and skill-naming tokens, improving success rates and reducing misleading skill exposure in long-horizon tasks.
FrameSkip is a data-layer frame selection method that improves Vision-Language-Action (VLA) policy training by prioritizing high-importance frames based on action variation and visual-coherence metrics, achieving a macro-average success rate of 76.15% across three benchmarks while using only 20% of unique frames.