A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet fully autonomous learning remains difficult with current policies: sparse rewards and weak zero-shot exploration often prevent online learning from discovering successful behavior. As a first step towards fully autonomous learning, we propose the minimal-data adaptation regime, in which a pretrained robot policy must learn a new task from as little as one demonstration followed by autonomous online interaction. We establish that, although pretrained policies fail zero-shot on many new tasks, a single demonstration suffices to direct their behavioral prior to coarse task-following behavior, albeit with unreliable performance. From this insight, we isolate the key design principles required to convert coarse task-following into reliable task success. These include (1) the surprising efficacy of pretrained VLA representations to support sample-efficient value learning from online interaction (2) the sufficiency of residual policy learning to learn corrective behavior and (3) the need for careful data-balancing to prevent exploration collapse. As a representative of these principles, we propose MiDAS, a simple offline-to-online RL recipe that combines few-demonstration behavior cloning with value-based residual learning over frozen VLA representations. Across LIBERO and RoboCasa tasks, MiDAS substantially improves performance from single-demonstration initializations, including policies that succeed only rarely, validating the principles highlighted above. On a bimanual YAM platform, starting from a brittle initial policy, MiDAS improves task success after approximately six hours of online interaction.

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