Abstract: The post-training phase of large language models (LLMs) is crucial for enhancing model's ability to follow instructions, improve reasoning, and align with human preferences. It also poses significant challenges, including the need for extensive high-quality training data and the risk of overfitting. Moreover, the model development process requires post-training and evaluation after every base model adjustment, which incurs significant computational costs. This paper introduces Param$\Delta$, an innovative approach that streamlines the post-training process by transferring knowledge and capability from an existing post-trained model to a newly upgraded base model without additional training. By capturing the latent knowledge embedded in model parameters, our approach effectively replicates the traditional post-training and achieves comparable performance with below 2\% performance gap (98\% of transfer efficiency). As a result, Param$\Delta$ facilitates an efficient iterative process in base model development and enables painless plug-and-play testing of the updated model's performance, offering a sustainable solution for continuous retraining cycles. Furthermore, Param$\Delta$ brings a new perspective on how to fully leverage models in the open-weight community, where checkpoints for base models and instruction-tuned models are readily available and frequently updated, by providing a cost-effective framework that has the potential to accelerate the iterative cycle of model development.