Hybrid LLM architectures that combine Attention and State Space Models (SSMs) achieve state-of-the-art accuracy and runtime performance. Recent work has demonstrated that applying compression and distillation to Attention-only models yields smaller, more accurate models at a fraction of the training cost. In this work, we explore the effectiveness of compressing Hybrid architectures. We introduce a novel group-aware pruning strategy that preserves the structural integrity of SSM blocks and their sequence modeling capabilities.

Behavior cloning, the dominant approach for training autonomous vehicle (AV) policies, suffers from a fundamental gap: policies trained open-loop on temporally independent samples must operate in closed-loop where actions influence future observations. This mismatch can cause covariate shift, compounding errors, and poor interactive behavior, among other issues. Closed-loop training mitigates the problem by exposing policies to the consequences of their actions during training.