Vision-language-action (VLA) models show promising knowledge accumulation ability through pretraining, yet continual learning remains challenging. Existing continual imitation learning (CIL) methods often require additional parameters or modules, limiting scalability for large VLA models. We propose Stellar VLA, a knowledge-driven CIL framework without parameter growth, featuring two variants: T-Stellar for task-centric modeling and TS-Stellar for hierarchical modeling of reusable skills across tasks. Stellar VLA jointly optimizes task representations and a self-evolving knowledge space, with knowledge-posterior-guided expert routing using knowledge relations and Top-K semantic embeddings for task specialization. Experiments on LIBERO demonstrate strong performance against VLA and CIL baselines with only 1% replay. Dual-arm real-world experiments validate knowledge transfer across embodiments and scenes. TS-Stellar excels in hierarchical manipulation, while visualizations demonstrate knowledge retention and task discovery. Project Website: https://stellarvla.github.io/

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