Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation

LoRA mixture-of-experts variants for continual learning in motion-language agents

A new arXiv preprint investigates continual learning for bidirectional motion-language agents that must both understand and generate human motion without catastrophic forgetting. The authors propose LoRA-based mixture-of-experts architectures with an autoencoder-based router for task-specific expert selection at inference time, requiring no task labels. Evaluated on a five-task benchmark derived from HumanML3D, the approach achieves near-zero forgetting across motion-to-text and text-to-motion directions. A key finding is that hard expert selection outperforms soft blending, and that token-level accuracy can diverge from downstream generation quality.