CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning

2026-05-22 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

CP-MoE is a continual learning framework designed to mitigate catastrophic forgetting in large language models (LLMs) and vision-language models (VLMs) using LoRA-based Mixture-of-Experts (MoE) architectures. Developed by Yang Liu, Toan Nguyen, and Flora D. Salim, it introduces a transient expert that captures early task-specific updates and guides their integration into stable experts. The framework incorporates a consistency-preserving routing bias, which uses the transient expert to estimate representation similarity and steer routing, and a transient expert-guided regularisation mechanism that selectively protects important historical parameters. Validated on SuperNI and VQA v2 benchmarks, CP-MoE achieved 50.84% Average Performance and 35.80% Zero-shot Transfer on SuperNI, and 62.30% Average Performance with -0.35% Average Forgetting on VQA v2, outperforming strong MoE baselines. It uses 1.48% of backbone parameters for SuperNI and 0.47% for VQA v2.

Key takeaway

For Machine Learning Engineers building continual learning systems with MoE architectures, CP-MoE offers a robust approach to mitigate catastrophic forgetting. You should consider implementing its transient expert for task-specific probing and its consistency-preserving routing bias to improve knowledge transfer. This method helps maintain high performance and strong zero-shot generalization without significant computational overhead, making your models more adaptive to evolving data streams.

Key insights

CP-MoE uses a transient expert and consistency-preserving mechanisms to reduce catastrophic forgetting and improve knowledge transfer in MoE continual learning.

Principles

• Transient experts probe task-specific updates.
• Route to experts with compatible representations.
• Selectively protect important historical parameters.

Method

CP-MoE adapts a transient expert on warm-up tokens to derive an importance mask and representation-consistency scores via CKA. These guide routing bias and weighted regularisation for stable experts.

In practice

• Implement a transient expert for task-specific probing.
• Use CKA to measure expert representation similarity.
• Apply importance-weighted parameter regularisation.

Topics

• Continual Learning
• Mixture-of-Experts
• Large Language Models
• Vision-Language Models
• Catastrophic Forgetting
• Parameter-Efficient Fine-Tuning
• LoRA

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

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