This paper introduces the Scalable Language Model (SLM), a novel approach to continual learning that enables efficient adaptation to new tasks without relying on experience replay, optimization constraints, or inference task-ID. SLM integrates two key components: Joint Adaptive Re-Parameterization (JARe) and Dynamic Task-related Knowledge Retrieval (DTKR). JARe dynamically adjusts the model's weights based on task distribution, while DTKR retrieves relevant knowledge for each task. This approach allows the model to adaptively re-parameterize pretrained models, achieving smooth and efficient continual learning. The method demonstrates state-of-the-art performance on various benchmarks, including BERT, T5, and LLaMA-2, with minimal forgetting and effective adaptation to both full-set and few-shot scenarios. SLM also extends the application of continual learning beyond single-task classification to diverse domains and task types, showcasing superior generalization ability. The method leverages vector space retrieval to enhance knowledge expansion and management, enabling the model to scale effectively across different tasks. Experiments show that SLM achieves up to 80% reduction in forgetting with only a minor performance degradation. The approach is implemented with a training pipeline that includes key-value pair generation and fine-tuning, ensuring efficient and effective adaptation to new tasks. The results demonstrate the effectiveness of SLM in various continual learning scenarios, making it a promising solution for practical applications.This paper introduces the Scalable Language Model (SLM), a novel approach to continual learning that enables efficient adaptation to new tasks without relying on experience replay, optimization constraints, or inference task-ID. SLM integrates two key components: Joint Adaptive Re-Parameterization (JARe) and Dynamic Task-related Knowledge Retrieval (DTKR). JARe dynamically adjusts the model's weights based on task distribution, while DTKR retrieves relevant knowledge for each task. This approach allows the model to adaptively re-parameterize pretrained models, achieving smooth and efficient continual learning. The method demonstrates state-of-the-art performance on various benchmarks, including BERT, T5, and LLaMA-2, with minimal forgetting and effective adaptation to both full-set and few-shot scenarios. SLM also extends the application of continual learning beyond single-task classification to diverse domains and task types, showcasing superior generalization ability. The method leverages vector space retrieval to enhance knowledge expansion and management, enabling the model to scale effectively across different tasks. Experiments show that SLM achieves up to 80% reduction in forgetting with only a minor performance degradation. The approach is implemented with a training pipeline that includes key-value pair generation and fine-tuning, ensuring efficient and effective adaptation to new tasks. The results demonstrate the effectiveness of SLM in various continual learning scenarios, making it a promising solution for practical applications.