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Sampling rollouts via GRPO actively degrades tool-selection training as policies concentrate, whereas exact policy optimization over enumerable combinatorial tool spaces eliminates vanishing gradients and boosts accuracy by up to 14.2 points.
Adaptive similarity margins in HN-CLIP boost retrieval accuracy by up to 4.3% while training 2.4x faster than leading methods.
