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#artificial intelligence Preprint Sep 2026

Learn from the Gap: Differential-Aware Advantage Pruning with Adaptive Rollout Sampling for GRPO

Recently, Group Relative Policy Optimization (GRPO) and its variants have been developed for policy optimization and demonstrated notable performance gains. However, these methods usually incur substantial computational overhead due to per-question multi-rollout sampling and repeated per-token probability evaluation ac...

Jia-Hua Yang, Zhiwei Yang, Xian-Peng Zhang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Training-Free Halving of Activated Experts in Fine-Grained Mixture-of-Experts Models

Modern fine-grained Mixture-of-Experts (MoE) models route each token to a small number of experts and renormalize their router probabilities. We show that this renormalization implicitly calibrates expert output gain to the training top-$k$: reducing $k$ at inference changes not only which experts are used but also the...

Xing Chen, Hengshuai Yao · 0 citations

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