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#artificial intelligence Preprint Sep 2026

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

This work proposes FastRL, a novel plug-and-play reinforcement learning framework that simultaneously improves training efficiency and the effectiveness of policy learning, and introduces an advantage-aware pruning strategy to selectively preserve high-advantage trajectories while maximizing inter-trajectory gradient d...

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

It is found that perplexity and downstream accuracy favor different MoE experts, cautioning against selecting MoE compression settings using unlabeled text alone, and showing that expert identity matters substantially more than expert weighting, while balanced and domain-specialized routing leaves limited room for expe...

Xing Chen, Heng-Shuai Yao · 0 citations

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