Avatar Forcing is a research framework for real-time, interactive head-avatar generation that conditions on multimodal user inputs (audio and motion) to produce expressive, reactive facial and head motion. It introduces Motion Latent Diffusion Forcing to enable causal, low-latency generation (reported ~500 ms) and a direct preference optimization method that uses synthetic "losing" samples (created by dropping user conditions) to learn expressive reactions without additional labeled data. The method is presented in an arXiv preprint and an official PyTorch implementation project page/repository; experiments report a 6.8x speedup over baselines and user preference rates over 80% versus baseline methods.