Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization

• URL: http://arxiv.org/abs/2602.14159v1
• Date: Sun, 15 Feb 2026 14:19:12 GMT
• Title: Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization
• Authors: Rizhen Hu, Yuan Cao, Boao Kong, Mou Sun, Kun Yuan,
• Abstract summary: We propose two plug-and-play regularization losses that enhance MoE specialization and routing efficiency.<n>We implement both losses as a drop-in Megatron-LM module.
• Score: 10.669680236190432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse Mixture-of-Experts (MoE) models scale Transformers efficiently but suffer from expert overlap -- redundant representations across experts and routing ambiguity, resulting in severely underutilized model capacity. While architectural solutions like DeepSeekMoE promote specialization, they require substantial structural modifications and rely solely on intra-layer signals. In this paper, we propose two plug-and-play regularization losses that enhance MoE specialization and routing efficiency without modifying router or model architectures. First, an intra-layer specialization loss penalizes cosine similarity between experts' SwiGLU activations on identical tokens, encouraging experts to specialize in complementary knowledge. Second, a cross-layer coupling loss maximizes joint Top-$k$ routing probabilities across adjacent layers, establishing coherent expert pathways through network depth while reinforcing intra-layer expert specialization. Both losses are orthogonal to the standard load-balancing loss and compatible with both the shared-expert architecture in DeepSeekMoE and vanilla top-$k$ MoE architectures. We implement both losses as a drop-in Megatron-LM module. Extensive experiments across pre-training, fine-tuning, and zero-shot benchmarks demonstrate consistent task gains, higher expert specialization, and lower-entropy routing; together, these improvements translate into faster inference via more stable expert pathways.

Related papers

• 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)
• Spectral Manifold Regularization for Stable and Modular Routing in Deep MoE Architectures [2.538209532048867]
  Mixture of Experts (MoE) architectures enable efficient scaling of neural networks but suffer from expert collapse.<n>We propose the Spectrally-Regularized Mixture of Experts (SR-MoE), which imposes geometric constraints on the routing manifold to enforce structural modularity.
  arXiv  Detail & Related papers  (2026-01-07T12:59:37Z)
• ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization [13.182475975397251]
  ERMoE is a sparse MoE transformer that replaces learned gating logits with an "Eigenbasis Score"<n>We show that ERMoE achieves state-of-the-art accuracy on ImageNet classification and cross-modal image-text retrieval benchmarks.<n>A 3D MRI variant (ERMoE-ba) improves brain age prediction accuracy by more than 7% and yields interpretable expert specializations.
  arXiv  Detail & Related papers  (2025-11-14T05:31:37Z)
• Mixture-of-Transformers Learn Faster: A Theoretical Study on Classification Problems [59.94955550958074]
  We study a tractable theoretical framework in which each transformer block acts as an expert governed by a continuously trained gating network.<n>We show that expert specialization reduces gradient conflicts and makes each subtask strongly convex.<n>We prove that the training drives the expected prediction loss to near zero in $O(log(epsilon-1)$ steps, significantly improving over the $O(epsilon-1)$ rate for a single transformer.
  arXiv  Detail & Related papers  (2025-10-30T21:07:36Z)
• ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts [25.46805026086543]
  We describe ReXMoE, a novel MoE architecture that improves routing beyond the existing layer-local approaches.<n>ReXMoE decouples expert dimensionality from per-layer budgets, enabling richer expert combinations without sacrificing individual expert capacity.
  arXiv  Detail & Related papers  (2025-10-20T12:27:55Z)
• Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs [54.95810313530111]
  DERN is a task-agnostic and retraining-free framework for expert pruning and reconstruction.<n>It improves performance by more than 5% on commonsense reasoning and MMLU benchmarks under 50% expert sparsity.
  arXiv  Detail & Related papers  (2025-09-12T16:09:39Z)
• Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers [10.912224105652044]
  Robustifying convolutional neural networks (CNNs) against adversarial attacks remains challenging.<n>We explore the use of sparse mixture-of-experts (MoE) layers to improve robustness.<n>We find that inserting a single MoE layer in the deeper stages leads to consistent improvements in robustness.
  arXiv  Detail & Related papers  (2025-09-05T13:25:33Z)
• RouteMark: A Fingerprint for Intellectual Property Attribution in Routing-based Model Merging [69.2230254959204]
  We propose RouteMark, a framework for IP protection in merged MoE models.<n>Our key insight is that task-specific experts exhibit stable and distinctive routing behaviors under probing inputs.<n>For attribution and tampering detection, we introduce a similarity-based matching algorithm.
  arXiv  Detail & Related papers  (2025-08-03T14:51:58Z)
• Advancing Expert Specialization for Better MoE [22.88847592702946]
  Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input.<n>We observe that the commonly used auxiliary load balancing loss often leads to expert overlap and overly uniform routing.<n>We propose a simple yet effective solution that introduces two complementary objectives.
  arXiv  Detail & Related papers  (2025-05-28T13:09:47Z)
• Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization [51.98792406392873]
  Mixture of Experts (MoE) provides a powerful way to decompose dense layers into smaller, modular computations. A major challenge lies in the computational cost of scaling the number of experts high enough to achieve fine-grained specialization. We propose the Multilinear Mixture of Experts ($mu$MoE) layer to address this, focusing on vision models.
  arXiv  Detail & Related papers  (2024-02-19T21:20:22Z)
• Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy [84.11508381847929]
  Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks. We propose M-SMoE, which leverages routing statistics to guide expert merging. Our MC-SMoE achieves up to 80% memory and a 20% FLOPs reduction, with virtually no loss in performance.
  arXiv  Detail & Related papers  (2023-10-02T16:51:32Z)
• MoEC: Mixture of Expert Clusters [93.63738535295866]
  Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation.
  arXiv  Detail & Related papers  (2022-07-19T06:09:55Z)

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.