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Can tiny camera images make sim-to-real reinforcement learning genuinely faster? This episode explains how Squint combines compact visual inputs with off-policy learning to accelerate robot-policy training. We cover three questions: why rendering first and downsampling later differs from directly rendering tiny images; how replay, distributional critics, and GPU execution fit together; and what the physical-robot evaluations establish. Squint trains separate manipulation policies in ManiSkill3 and deploys them on an SO-101 arm using a wrist camera and proprioception. Its task suite covers reaching, lifting, placing, and stacking cubes or cans. The paper reports 91.3 percent real-world success after 15 minutes of simulation training per policy. We connect the approach to FastTD3 and FastSAC, inspect the open implementation, and explain its strongest practical limitation: the evidence comes from a visually engineered, closely matched workspace. The authors' own deployment guidance shows why that context matters. For roboticists optimizing experiment turnaround, the episode separates the learning mechanisms from the transfer setup. How much of your visual RL pipeline is paying for detail the controller does not need?
