High Dynamic Range (HDR) imaging seeks to enhance image quality by combining multiple Low Dynamic Range (LDR) images captured at varying exposure levels. Traditional deep learning approaches often employ reconstruction loss, but this method can lead to ambiguities in feature space during training. To address this issue, we present a new loss function, termed Gravitated Latent Space (GLS) loss, that leverages a metric tensor to introduce a form of virtual gravity within the latent space. This feature helps the model in overcoming saddle points more effectively. Easy to integrate, the GLS loss function fosters stable learning within a convex environment and demonstrates its performance in improving HDR image quality. Experimental data confirms that the proposed method outperforms existing state-of-the-art techniques in quantitative evaluations.