MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models

• URL: http://arxiv.org/abs/2603.24984v2
• Date: Sun, 29 Mar 2026 06:59:34 GMT
• Title: MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models
• Abstract summary: Mixture-of-Experts (MoE) has emerged as an effective approach to reduce the computational overhead of Transformer architectures.<n>We propose MoE-GRPO, a reinforcement learning framework for optimizing expert routing in MoE-based Vision-Language Models.<n>We show that MoE-GRPO consistently outperforms standard top-K routing and its variants by promoting more diverse expert selection.
• Score: 36.67777831459829
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mixture-of-Experts (MoE) has emerged as an effective approach to reduce the computational overhead of Transformer architectures by sparsely activating a subset of parameters for each token while preserving high model capacity. This paradigm has recently been extended to Vision-Language Models (VLMs), enabling scalable multi-modal understanding with reduced computational cost. However, the widely adopted deterministic top-K routing mechanism may overlook more optimal expert combinations and lead to expert overfitting. To address this limitation and improve the diversity of expert selection, we propose MoE-GRPO, a reinforcement learning (RL)-based framework for optimizing expert routing in MoE-based VLMs. Specifically, we formulate expert selection as a sequential decision-making problem and optimize it using Group Relative Policy Optimization (GRPO), allowing the model to learn adaptive expert routing policies through exploration and reward-based feedback. Furthermore, we introduce a modality-aware router guidance that enhances training stability and efficiency by discouraging the router from exploring experts that are infrequently activated for a given modality. Extensive experiments on multi-modal image and video benchmarks show that MoE-GRPO consistently outperforms standard top-K routing and its variants by promoting more diverse expert selection, thereby mitigating expert overfitting and enabling a task-level expert specialization.

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