LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling

• URL: http://arxiv.org/abs/2606.04438v1
• Date: Wed, 03 Jun 2026 04:38:12 GMT
• Title: LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling
• Abstract summary: We present LoopMoE, a looped MoE language model that integrates sparse routing with iterative weight-shared computation.<n>Designs enable the first strictly controlled, head-to-head evaluation of a looped MoE against a Vanilla MoE.
• Score: 36.7482623230111
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mixture-of-Experts (MoE) and looped architectures scale models along two orthogonal axes, namely parameter capacity and effective depth. However, mainstream looped architectures rely on dense backbones that couple parameter count with per-token FLOPs, which makes it impossible to isolate the effect of iterative computation under matched budgets. To this end, we present LoopMoE, a looped MoE language model that integrates sparse routing with iterative weight-shared computation through two designs. The first is IterAdaLN, which resolves weight-sharing symmetry via a modulation signal jointly conditioned on the iteration index and the per-token hidden state. The second is a capacity-balancing strategy that recovers the attention-to-FFN active parameter ratio of well-tuned non-looped references. Together, these designs enable the first strictly controlled, head-to-head evaluation of a looped MoE against a Vanilla MoE under identical total parameters, per-token FLOPs, and active sublayer ratios. At the 3B scale, LoopMoE outperforms the Vanilla MoE on 8 of 9 downstream benchmarks with an average improvement exceeding 1 point. At the 9B scale, LoopMoE continues to outperform the matched Vanilla MoE, indicating that the architectural gain persists at larger scale. Our work establishes a controlled synthesis of sparsity and recurrence, and suggests a promising direction for looped language models.

Related papers

• Training Communication-Efficient Mixture-of-Experts Language Models with Layer Re-Configuration [13.010351659575297]
  Communication-efficient MoE models (CE-MoE)<n>Adopt a heterogeneous layer pattern that decouples token-mixing and channel-mixing depth.<n>At the 31.5B scale, CE-MoE uses 33.3% fewer GPU-hours while improving average downstream score and inference throughput.
  arXiv  Detail & Related papers  (2026-08-28T16:44:50Z)
• LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models [129.64558694024126]
  Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling.<n>We characterize how optimization hyper parameters, compute allocation, and architecture scale for MoE dLLMs.<n>We train LLaDA MoE v2, a 30B-A3B dLLM, from scratch on 23.5T tokens.
  arXiv  Detail & Related papers  (2026-08-04T10:53:02Z)
• Looped State-Space Language Models with Adaptive Exit-State Selection [43.98434319030707]
  Recent work on looped language models suggests that many reasoning problems benefit from greater computational depth rather than additional independent parameters.<n>We investigate Looped Mamba and Looped Hybrid Mamba-Transformer architectures, which repeatedly apply a shared Mamba block to introduce explicit finite-depth recurrent computation.<n>We adapt Ouro's two-stage exit gate to Looped Mamba for threshold-controlled selection among recurrent-step outputs.
  arXiv  Detail & Related papers  (2026-07-11T04:09:19Z)
• cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs [3.50654760771471]
  cMoLLM is a convolutionally gated mixture-of-LLMs that routes over end-to-end streams through fully differentiable dynamic convolution.<n>In GPT-2-style models trained on FineWeb, cMoLLM improves language modeling perplexity and downstream GLUE and SQuAD accuracy under matched compute.
  arXiv  Detail & Related papers  (2026-06-06T13:51:15Z)
• LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models [56.999481798138625]
  LimiX-2M is a 2M- parameter model that outperforms larger TabPFN-v2 and TabICL baselines on widely used benchmarks.<n>We present a unified tokenize-and-route framework for strong Tabular foundation models (TFMs)<n>Results highlight value-aware tokenization and readout-aligned routing as key levers for improving the accuracy--efficiency trade-off in TFMs.
  arXiv  Detail & Related papers  (2026-06-03T06:07:33Z)
• JTok: On Token Embedding as another Axis of Scaling Law via Joint Token Self-modulation [46.64215658042213]
  We introduce Joint-Token (JTok) and Mixture of Joint-Token (JTok-M), which augment Transformer layers with modulation vectors retrieved from auxiliary embedding tables.<n>These vectors modulate the backbone via lightweight, element-wise operations, incurring negligible FLOPs overhead.<n>Our approach consistently reduces validation loss and significantly improves downstream task performance.
  arXiv  Detail & Related papers  (2026-01-31T16:15:18Z)
• Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models [25.608085561102566]
  We introduce Leverage Efficiency (EL), a metric quantifying the computational advantage of an MoE model over a dense equivalent.<n>EL is driven by the expert activation ratio and the total compute budget, both following predictable power laws.<n>We integrate these discoveries into a unified scaling law that accurately predicts the EL of an MoE architecture based on its configuration.
  arXiv  Detail & Related papers  (2025-07-23T17:10:23Z)
• Comba: Improving Bilinear RNNs with Closed-loop Control [57.800320390698516]
  We introduce the concept of Bilinear RNNs with a comprehensive analysis on the advantages and limitations of these models.<n>We propose a novel Bilinear RNN variant named Comba, which adopts a scalar-plus-low-rank state transition, with both state feedback and output feedback corrections.<n>We also implement a hardware-efficient chunk-wise parallel kernel in Triton and train models with 340M/1.3B parameters on large-scale corpus.
  arXiv  Detail & Related papers  (2025-06-03T05:44:50Z)
• Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs [111.69640966866059]
  Sparse large language models (LLMs) with Mixture of Experts (MoE) and close to a trillion parameters are dominating the realm of most capable language models.<n>In this paper, we aim to uncover a recipe to harness such scale on Ascend NPUs.<n>The key goals are better usage of the computing resources under the dynamic sparse model structures and materializing the expected performance gain on the actual hardware.
  arXiv  Detail & Related papers  (2025-05-07T15:46:36Z)
• ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration [61.579842548990754]
  Mixture-of-Experts (MoE) Transformer, the backbone of multiple phenomenal language models, leverages sparsity by activating only a fraction of model parameters for each input token.<n>We introduce ResMoE, an innovative MoE approximation framework that utilizes Wasserstein barycenter to extract a common expert (barycenter expert) and approximate the residuals between this barycenter expert and the original ones.
  arXiv  Detail & Related papers  (2025-03-10T03:15:54Z)
• EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference [49.94169109038806]
  This paper introduces EPS-MoE, a novel expert pipeline scheduler for MoE that surpasses the existing parallelism schemes.<n>Our results demonstrate at most 52.4% improvement in prefill throughput compared to existing parallel inference methods.
  arXiv  Detail & Related papers  (2024-10-16T05:17:49Z)
• Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules [96.21649779507831]
  We propose a novel architecture dubbed mixture-of-modules (MoM) MoM is motivated by an intuition that any layer, regardless of its position, can be used to compute a token. We show that MoM provides not only a unified framework for Transformers but also a flexible and learnable approach for reducing redundancy.
  arXiv  Detail & Related papers  (2024-07-09T08:50:18Z)
• QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts [47.01697456105496]
  Mixture-of-Experts (MoE) is a promising way to scale up the learning capacity of large language models.<n>MoE suffers from significant memory overheads due to the vast parameter size.<n>Post-training quantization offers a powerful approach for model compression.
  arXiv  Detail & Related papers  (2024-06-12T12:44:48Z)

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.