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SMILE: Smooth Motion for Improved Long-Horizon VLA Execution
AI InsightSMILE predicts B-spline coefficients and decodes them into smooth action sequences, changing only the action representation rather than the model architecture to extend fixed horizons for VLA models. Compared with prior approaches that relied on larger models or more inference calls to improve long-horizon accuracy, it improves accuracy and amortized inference efficiency across four baselines including SmolVLA and Evo1, with SMILE-Evo1 achieving 98.0% and 1.1x speedup on LIBERO. This suggests B-spline smoothed action representation could become a low-cost universal improvement for long-horizon VLA execution.Key TakeawayUnlike extending models or adding inference, SMILE replaces action representation with B-splines to boost long-horizon accuracy and speed.Why It MattersLong-horizon VLA degradation has often been blamed on model capacity; SMILE proves action representation is a key variable, yielding significant efficiency gains with near-zero architecture changes.Who's Affected- AI ResearchersGain a plug-in action representation method to improve long-horizon VLA without retraining backbone models.
- Robotics ResearchersReal-world experiments validate smooth action sequences for execution stability, transferable to robot control pipelines.
- Vla Model DevelopersCan directly replace the action decoding layer to improve benchmark scores and inference speed at low cost.
What's NextWatch whether SMILE becomes default in subsequent VLA work and whether B-spline representation generalizes to more complex action spaces and longer task chains.Importance 72/100