Training-Inference Discrepancies in MoE Models

Updated 15 October 2025

Training-Inference Discrepancies in MoE Models are characterized by mismatches in expert activation and token routing between training and inference due to system bottlenecks and capacity limits.
Regularization methods like random token selection, aggregation of experts, and auxiliary load-balancing losses are used to mitigate biases and improve convergence during training.
System-level optimizations—including expert pruning, task-level routing, and multi-dimensional parallelism—enhance scalability and inference efficiency in practical deployments.

Mixture-of-Experts (MoE) models are a class of sparsely activated deep learning architectures in which a routing mechanism selects a small subset of ‘experts’ (typically individual feed-forward modules) from a large pool for each input. Although this approach enables MoEs to achieve sublinear scaling of computational cost relative to parameter count, it introduces inherent discrepancies between training and inference phases. These differences arise from system-scale bottlenecks, routing behavior, expert utilization, and parameter management, affecting both model robustness and deployment efficiency.

1. System and Modeling Challenges in MoE Training and Inference

A central challenge in MoE system design is the need to scale both the shared backbone (“non-expert” base) and the conditional expert layers while respecting hardware and memory limitations. Standard data and model parallelism methods—adequate for dense models—are insufficient. For MoEs, expert parallelism must be carefully orchestrated alongside other forms of parallelism to avoid wasted memory (from unnecessary replication) and routing inefficiencies.

A practical difficulty is the limited expert capacity during training: given batch and routing constraints, not all tokens can be assigned to their preferred experts. This results in nonuniform expert activation and token drops, especially for later-position tokens, causing a prefix bias. This mismatch leads to differences between the distribution of token-expert assignments at training (where capacity-induced overflows or underutilization may occur) and inference (where batch sizes and routing dynamics may differ), thus producing training-inference discrepancies (Kim et al., 2021).

2. Approaches to Regularizing Expert Utilization and Routing

To address these mismatches, several strategies have been proposed:

Random Token Selection (RTS): Instead of processing tokens in sequence order (which can bias prefix tokens over later positions, especially when capacity is exceeded), RTS randomizes token assignment order before routing. This acts as a regularizer that encourages fairer expert utilization and more robust convergence, thereby reducing bias in representation learning (Kim et al., 2021).
Aggregation of Experts (AoE): Models can leverage checkpoints from earlier stages by aggregating experts and concatenating gating matrices. This “merged” initialization improves convergence when resuming or fine-tuning, which stabilizes expert representations and eases transition to new inference workloads.
Auxiliary Load-Balancing Losses: Such losses promote uniform expert utilization, mitigating the risk of expert collapse during training. However, uniform activation at training can lead to scenarios where many experts remain dormant at inference—especially if the data distribution at deployment is more concentrated or less diverse than during training (Chernov, 24 Feb 2025).

Despite these methods, analysis shows that in practical inference settings (e.g., quiz-style tasks), only a small subset of experts may be activated, and gating outputs may be almost uniform within the Top-K, preventing sharp specialization (Chernov, 24 Feb 2025).

3. Expert Pruning, Task-Level Routing, and Elastic Inference

To concretely reduce the gap between training and inference, multiple techniques have proven effective:

Expert Pruning: By tracking expert utilization (e.g., frequency of activation over a validation set), experts with low utility can be pruned before inference, thereby reducing computational overhead without significant performance loss. This pruning can be random or, more effectively, utilization-based. Post-pruning fine-tuning adapts the remaining experts, mitigating performance drops from structural changes (Kim et al., 2021).
Task-Level Routing: Rather than dynamically routing each token, task-level routing assigns expert sub-networks based on a coarse granularity (e.g., sentence or task identifier), so that all tokens in a sentence or task share the same expert pathway. This reduces both the memory footprint (as only a subset of experts are active per task) and the inter-device communication cost. Notably, task-level routing preserves the quality gains of token-level MoE while boosting inference throughput (e.g., a 1.9× throughput improvement in task-level vs. token-level routing for 32-expert models) (Kudugunta et al., 2021).
Elastic and Matryoshka MoE Training: Methods such as Matryoshka MoE train with systematically varying numbers of active experts, instilling a coarse-to-fine hierarchy. The router learns to rank experts so that, during inference, models can run with fewer experts for efficiency or more for accuracy, maintaining a stable ranking and functional specialization. Layerwise randomization strategies further encourage robust specialization and allow efficient deployment across varied computational regimes (Wang et al., 30 Sep 2025).
Capacity-Aware Inference: Inference-time strategies, such as Capacity-Aware Token Drop and Expanded Drop (Token Reroute), set a maximum capacity per expert in line with expected average load. Excess tokens assigned to overloaded experts are either dropped or rerouted to underused ones. This mitigates the “straggler effect”—where synchronization is bottlenecked by the slowest (most burdened) expert—enabling up to ~1.9× speedup with negligible degradation in accuracy (He et al., 7 Mar 2025).

4. System-Level Solutions: Multi-Dimensional Parallelism and Communication Optimization

Scalable distributed MoE training and inference depend on sophisticated parallelism and communication schemes:

DeepSpeed and MegaScale-MoE: DeepSpeed leverages five forms of parallelism—including expert, model, data, ZeRO, and ZeRO-Offload—partitioning memory and computation to enable trillion-parameter models on commodity hardware (Kim et al., 2021). MegaScale-MoE further customizes sequence parallelism for attention and expert parallelism for FFN layers. Key formulas (e.g., communication volume for tensor parallelism: $V_{TP} = 2 b s h (n-1) / n$ ) guide communication-efficient design (Jin et al., 16 May 2025).
Computation-Communication Overlap and Compression: Both DeepSpeed and MegaScale-MoE implement overlapping strategies—executing communication with computation at both operator and kernel level, plus activation rematerialization (only storing “critical” activations). Communication compression (using BF16 or FP8) cuts data transfer volume with minimal convergence impact (Jin et al., 16 May 2025).
All-to-All Communication and Resource Scheduling: Approaches such as Lina decompose tensors into micro-ops, prioritize all-to-all over allreduce operations, and dynamically reassign resources during inference based on expert “popularity,” as estimated by token routing probabilities:

$n_e = N \times \frac{1}{N_t} \sum_{t=1}^{N_t} P^{i+1}_{j(t)}(e)$

This reduces training step times by up to 1.73× and inference latency by up to 1.63× over previous best systems (Li et al., 2022).

Expert Sharding: MoEShard shards every expert’s parameters across all GPUs. Each GPU processes its shard for all experts—aggregating the results in a way that guarantees perfect load balancing and full token retention, even under highly skewed token-to-expert assignments. This approach achieves up to 6.4× improvement in inference latency compared to DeepSpeed in encoder-based architectures (Balmau et al., 11 Mar 2025).

5. Quantitative Performance Assessment and Scaling Law Adaptation

Evaluation of MoE models increasingly considers both training and inference perspectives:

Throughput improvements are near-linear with GPU count for well-parallelized systems, with over 120 TFLOPS per GPU (hidden size 4096, batch 8192) (Kim et al., 2021).
In translation tasks, BLEU score improvements of ~1.37 (non-English centric sets) are attributed to scaled-up MoE systems (Kim et al., 2021).
MoE models with more experts converge significantly faster (e.g., a 64-expert MoE reaches a dense model’s cross-entropy loss in one-tenth the update steps) (Kim et al., 2021).
For multitask setups with machine translation and autoencoding, the training objective is additive: $L = C_{MT} + L_{DAE}$ .
Recent scaling laws now accommodate expert count E as well as model size and data; for example,

$\log L(N, D, E) = \log\left( \frac{A}{N^\alpha} + \frac{B}{\tilde{E}^\beta} + \frac{C}{D^\gamma} + F \right) + d \log N \log \tilde{E}$

Practitioners are encouraged to balance training and inference metrics (e.g., “cost per token”) in model design (Yun et al., 3 Apr 2024), sometimes favoring over-training smaller MoEs with more experts for better real-world serving costs.

6. Frameworks and Reproducibility

The DeepSpeed library is foundational for implementing efficient distributed MoE training. Its features include simplified APIs for integrating MoE layers, advanced memory management via ZeRO/ZeRO-Offload, and seamless orchestration of the discussed parallelism approaches (Kim et al., 2021). Open sourcing of code, model weights, and evaluation protocols further ensures that results (such as those in Nomic Embed v2) are reproducible and extensible for both training and inference research (Nussbaum et al., 11 Feb 2025).

7. Open Issues and Future Directions

Ongoing research addresses several persistent gaps:

The discrepancy between uniform expert utilization at training (due to auxiliary losses) and sparse, skewed utilizations at inference, suggesting opportunities in adaptive gating or utilization-based expert pruning (Chernov, 24 Feb 2025).
The need for elastic inference—flexibly adapting the number of active experts to serving conditions—has spurred methods such as Matryoshka MoE and Elastic MoE, which train over variable expert counts to robustly generalize across inference budgets (Wang et al., 30 Sep 2025, Gu et al., 26 Sep 2025).
Reproducible and efficient routing alignment has direct implications for MoE models using reinforcement learning; for example, Rollout Routing Replay (R3) records routing decisions at inference and replays them during training, reducing KL divergence and stabilizing policy optimization (Ma et al., 13 Oct 2025).
As models scale further, operator scheduling, kernel fusion, and hierarchical expert selection are active areas for improving training–inference consistency and hardware utilization at extreme scales (Jin et al., 16 May 2025).

Training-inference discrepancies in Mixture-of-Experts models fundamentally arise from differing system architectures, routing dynamics, and efficiency considerations. By combining novel routing regularizers, elastic and specialized training strategies, system-level optimizations, and adaptive pruning or task-level routing, recent work has succeeded in bringing model behavior at inference substantially closer to that at training. Ongoing advances in scaling law characterization, efficient sharding, communication-computation overlap, and reproducible frameworks are expected to further narrow this gap as MoE LLMs scale toward the next generation of foundational AI models.