The paper investigates how generative behavioral-cloning policies handle multimodal expert behavior, identifying bottlenecks in both latent-variable and action-space policy designs. For latent-variable policies, preserving demonstrated modes depends on action-conditioned latent representations, and excessive posterior-prior regularization can suppress this information. In action-space generative policies, multimodality is limited by the smoothness of the base-to-action transport, requiring either sharp transitions or off-support bridge regions to capture many well-separated modes. Experiments on synthetic navigation and a physical-robot manipulation task confirm these findings, while standard robotic simulation benchmarks show limited conditional multimodality, making deterministic regression competitive.
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