Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models

2025-06-20 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

Value-and-Structure Routing Alignment for Quantization (VSRAQ) is a novel post-training quantization objective specifically designed for Mixture-of-Experts (MoE) models. It addresses the critical issue of routing instability, where quantization-induced perturbations can alter top-$k$ expert selection and degrade model quality. VSRAQ achieves this by combining two complementary components: value alignment, which matches routing-relevant logits or scores with sensitivity-aware weighting, and structure alignment, which preserves expert ordering and decision boundaries between selected and non-selected experts. This method integrates into existing quantization frameworks like AutoRound, introduces no inference-time overhead, and consistently improves expert-selection consistency and reduces performance degradation. Experiments on MoE foundation models, including Solar-Open-100B (W4A16, NVFP4) and Nemotron-3-Nano-30B-A3B (NVFP4), demonstrate VSRAQ's superior performance over reconstruction-only and router-aware baselines, showing over an 11.29% relative improvement in expert-selection agreement.

Key takeaway

For machine learning engineers deploying Mixture-of-Experts (MoE) models, integrating VSRAQ into your post-training quantization pipelines is crucial. This method significantly reduces performance degradation by preserving routing consistency, especially for generation-based tasks where routing mismatches accumulate. You should apply VSRAQ to improve the reliability and efficiency of your quantized MoE models, particularly with W4A16 and NVFP4 settings, as it introduces no inference-time overhead. Consider its potential for more aggressive 2-bit or 3-bit quantization regimes.

Key insights

MoE quantization requires preserving both router output values and structural relationships to maintain expert selection consistency and mitigate performance degradation.

Principles

• MoE quantization is sensitive to routing instability.
• Preserving router output structure is critical for stability.
• Sigmoid-based routing benefits from sensitivity-aware weighting.

Method

VSRAQ augments post-training quantization (PTQ) frameworks with a router alignment loss, combining sensitivity-aware value alignment and structural alignment to preserve expert ordering and top-$k$ decision boundaries.

In practice

• Apply VSRAQ as a plug-in calibration objective.
• Use VSRAQ for W4A16 and NVFP4 quantization.
• Prioritize VSRAQ for generation-based tasks.

Topics

• Mixture-of-Experts
• Post-Training Quantization
• Routing Stability
• Large Language Models
• Model Compression
• Deep Learning Optimization

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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• Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models — VSRAQ quantizes MoE models by aligning routing-relevant values and expert structure to prevent expert-selection instability.
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• Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models — MoE model multilingual performance improves by aligning target-language routing with English expert activation in middle layers.
• KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models — KBVQ-MoE significantly improves ultra-low-bit quantization for MoE LLMs by eliminating expert redundancy and correcting output bias.
• CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning — CP-MoE uses a transient expert and consistency-preserving mechanisms to reduce catastrophic forgetting and improve knowledge transfer in MoE continual learning.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.