Sparse Activation Attributes for Frame Curation and Curriculum Scheduling in Imitation Learning

Training vision-language-action (VLA) models requires large-scale robot demonstration datasets, making data curation critical for both data efficiency and policy performance. Existing approaches typically select entire episodes or coarse temporal blocks and sample the retained data from a fixed distribution, leaving substantial frame-level redundancy and overlooking how the value of data with different attributes evolves throughout training. We introduce Sparse Activation Attributes-inspired Frame Curation and Curriculum Scheduling strategy (SAFari), a fine-grained framework that uses sparse activation attributes to jointly select training frames and schedule their presentation. SAFari first employs a sparse autoencoder to extract intermediate activation features and characterizes them by their generality and specificity. It then uses the strengths of these two attributes to assess individual frames and independently constructs general and specific frame streams. Finally, a step-wise sigmoid curriculum dynamically shifts the sampling emphasis from general to specific frames as training progresses. In this way, SAFari not only prioritizes informative frames at a fine-grained level, but also presents data with different attributes at appropriate stages of training through curriculum scheduling. Experiments across multiple VLA models in both simulation and real-robot settings demonstrate consistent performance gains. Moreover, SAFari maintains strong performance with only half the training steps, substantially improving training efficiency.