Author

Kayhan Behdin

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#artificial intelligence Preprint Oct 2026

Reconstruction-Optimized Expert Pruning for Mixture-of-Experts Language Models

Mixture-of-Experts (MoE) models reduce computation through sparse expert activation, but deployment still requires storing a large number of expert parameters. We study post-training expert pruning using task-domain calibration data as a subset-selection problem: given a pretrained MoE layer, select a fixed number of e...

Jelena Markovic-Voronov, P. Prastakos, Kayhan Behdin et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling

Asynchronous reinforcement-learning post-training of large language models is computationally efficient but inherently off-policy: the learner updates on rollouts generated by a stale policy, while correcting this mismatch requires importance weights that can be extremely heavy-tailed. A small number of trajectories ma...

Shayan Mohajer Hamidi, Kayhan Behdin, Wen-Hui Zhu et al. · 0 citations

Jul 2026

TREK: Distill to Explore, Reinforce to Refine

TREK (Teacher-Routed Exploration via Forward KL), a simple staged procedure that uses distillation not for imitation but for exploration support expansion, achieves high success rates early in training while unaided GRPO requires substantially more optimization steps to reach comparable levels.

Yuanda Xu, Zhengze Zhou, Kayhan Behdin et al. · arXiv.org · 0 citations