In Dreamer V1, the KL divergence term is used to constrain the distance between the posterior distribution q(z_t|h_t,o_t) and the prior distribution p(z_t|h_t); if the KL weight is too large, the posterior is forced to approach the prior, causing the latent variables to lose observational information (information loss), and if the weight is too small, the transition model cannot generate meaningful imagined trajectories from the prior (posterior collapse).