  Significant progress has been made in training multimodal trajectory
forecasting models for autonomous driving. However, effectively integrating
these models with downstream planners and model-based control approaches is
still an open problem. Although these models have conventionally been evaluated
for open-loop prediction, we show that they can be used to parameterize
autoregressive closed-loop models without retraining. We consider recent
trajectory prediction approaches which leverage learned anchor embeddings to
predict multiple trajectories, finding that these anchor embeddings can
parameterize discrete and distinct modes representing high-level driving
behaviors. We propose to perform fully reactive closed-loop planning over these
discrete latent modes, allowing us to tractably model the causal interactions
between agents at each step. We validate our approach on a suite of more
dynamic merging scenarios, finding that our approach avoids the $\textit{frozen
robot problem}$ which is pervasive in conventional planners. Our approach also
outperforms the previous state-of-the-art in CARLA on challenging dense traffic
scenarios when evaluated at realistic speeds.
