Teacher-Guided Routing for Sparse Vision Mixture-of-Experts

• URL: http://arxiv.org/abs/2604.21330v1
• Date: Thu, 23 Apr 2026 06:34:10 GMT
• Title: Teacher-Guided Routing for Sparse Vision Mixture-of-Experts
• Abstract summary: We propose TGR-MoE: Teacher-Guided Routing for Sparse Vision Mixture-of-Experts.<n>It stabilizes router learning using supervision derived from a pretrained dense teacher model.<n>It consistently improves both accuracy and routing consistency, while maintaining stable training even under highly sparse configurations.
• Score: 25.423413775524704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in deep learning has been driven by increasingly large-scale models, but the resulting computational cost has become a critical bottleneck. Sparse Mixture of Experts (MoE) offers an effective solution by activating only a small subset of experts for each input, achieving high scalability without sacrificing inference speed. Although effective, sparse MoE training exhibits characteristic optimization difficulties. Because the router receives informative gradients only through the experts selected in the forward pass, it suffers from gradient blocking and obtains little information from unselected routes. This limited, highly localized feedback makes it difficult for the router to learn appropriate expert-selection scores and often leads to unstable routing dynamics, such as fluctuating expert assignments during training. To address this issue, we propose TGR-MoE: Teacher-Guided Routing for Sparse Vision Mixture-of-Experts, a simple yet effective method that stabilizes router learning using supervision derived from a pretrained dense teacher model. TGR-MoE constructs a teacher router from the teacher's intermediate representations and uses its routing outputs as pseudo-supervision for the student router, suppressing frequent routing fluctuations during training and enabling knowledge-guided expert selection from the early stages of training. Extensive experiments on ImageNet-1K and CIFAR-100 demonstrate that TGR consistently improves both accuracy and routing consistency, while maintaining stable training even under highly sparse configurations.

Related papers

• Focusing on What Matters: Saliency-Harnessing Accurate Routing for Diffusion MoE [49.45064510462232]
  Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling diffusion models in visual generation.<n>We propose SharpMoE, a post-training framework with a saliency-harnessing accurate routing mechanism.<n>By bypassing the noise-distorted inputs, SharpMoE provides the router with clear saliency guidance.
  arXiv  Detail & Related papers  (2026-06-25T12:13:40Z)
• PACT: Privileged Trace Co-Training for Multi-Turn Tool-Use Agents [57.69188221227875]
  We propose a Privileged trAce Co-Training framework for multi-turn tool-use agents.<n>The key idea is to use expert traces only as training-time optimization signals rather than rollout-time hints.<n>We show that PACT consistently improves over strong SFT- and RL-based baselines.
  arXiv  Detail & Related papers  (2026-06-15T04:46:23Z)
• SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning [83.66308307152808]
  We propose StAbilized Mixture-of-Experts (SAME) for Multimodal Continual Instruction Tuning (MCIT)<n>SAME stabilizes expert selection by decomposing routing dynamics into subspaces and updating only task-relevant directions.<n>It also introduces adaptive expert activation to freeze selected experts during training, reducing redundant and cross-task interference.
  arXiv  Detail & Related papers  (2026-02-02T11:47:06Z)
• L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts [49.90176890917986]
  We propose Low-rank & Lipschitz-controlled Routing (L2R), a unified routing framework that reshapes both the routing space and scoring geometry.<n>L2R performs expert assignment in a shared low-rank latent routing space and introduces Saturated Inner-Product Scoring (SIPS) to explicitly control the Lipschitz behavior of routing functions.<n>Experiments on a large-scale language MoE model and a vision MoE setting on ImageNet demonstrate that L2R consistently improves routing stability, expert specialization, and overall model performance.
  arXiv  Detail & Related papers  (2026-01-29T07:18:33Z)
• ProxRouter: Proximity-Weighted LLM Query Routing for Improved Robustness to Outliers [14.831117443453165]
  Large language model (LLM) query routers are critical to modern AI platforms.<n>We propose Prox, which applies an exponentially tilted aggregation mechanism to balance bias and variance in nonparametric routers.
  arXiv  Detail & Related papers  (2025-10-10T20:28:14Z)
• xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning [104.63494870852894]
  We present x, a tool-calling-based routing system in which a learned router can either answer directly or invoke one or more external models.<n>Our implementation encompasses the full reinforcement learning framework, including reward and cost accounting.<n>Across diverse benchmarks, x achieves strong cost-performance trade-offs.
  arXiv  Detail & Related papers  (2025-10-09T16:52:01Z)
• Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts [11.437368205968573]
  This paper advances MoE theory by providing convergence guarantees for joint training of soft-routed MoE models with non-linear routers and experts.<n>We show that a post-training pruning can effectively eliminate redundant neurons, followed by a provably convergent fine-tuning process that reaches global optimality.
  arXiv  Detail & Related papers  (2025-10-08T16:40:31Z)
• Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition [3.616706632062255]
  We propose textbfDQRoute, a modular framework that combines difficulty-aware optimization with dynamic expert collaboration.<n>DQRoute first estimates class-wise difficulty based on prediction uncertainty and historical performance.<n>At inference time, expert predictions are weighted by confidence scores derived from expert-specific OOD detectors, enabling input-adaptive routing.
  arXiv  Detail & Related papers  (2025-08-27T07:09:00Z)
• Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning [125.61772424068903]
  Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. We present a model-agnostic training paradigm, called Progressive Perturbation-aware Contrastive Learning (PROPER) to enhance the generalization ability of existing VLN agents.
  arXiv  Detail & Related papers  (2024-03-09T02:34:13Z)
• Soft Merging of Experts with Adaptive Routing [38.962451264172856]
  We introduce Soft Merging of Experts with Adaptive Routing (SMEAR) SMEAR avoids discrete routing by using a single "merged" expert constructed via a weighted average of all of the experts' parameters. We empirically validate that models using SMEAR outperform models that route based on metadata or learn sparse routing through gradient estimation.
  arXiv  Detail & Related papers  (2023-06-06T15:04:31Z)
• StableMoE: Stable Routing Strategy for Mixture of Experts [109.0602120199226]
  Mixture-of-Experts (MoE) technique can scale up the model size of Transformers with an affordable computational overhead. We propose StableMoE with two training stages to address the routing fluctuation problem. Results show that StableMoE outperforms existing MoE methods in terms of both convergence speed and performance.
  arXiv  Detail & Related papers  (2022-04-18T16:48:19Z)
• Auto-Rectify Network for Unsupervised Indoor Depth Estimation [119.82412041164372]
  We establish that the complex ego-motions exhibited in handheld settings are a critical obstacle for learning depth. We propose a data pre-processing method that rectifies training images by removing their relative rotations for effective learning. Our results outperform the previous unsupervised SOTA method by a large margin on the challenging NYUv2 dataset.
  arXiv  Detail & Related papers  (2020-06-04T08:59:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.