Title: FLOAT:  FAST LEARNABLE ONCE-FOR-ALL ADVERSARIAL TRAINING FOR TUNABLE TRADE-OFF BETWEEN ACCURACY AND ROBUSTNESS. Abstract: Training a model that can be robust against adversarially-perturbed images with-out compromising accuracy on clean-images has proven to be challenging. Recent research has tried to resolve this issue by incorporating an additional layer after each  batch-normalization  layer  in  a  network,  that  implements  feature-wise  linear modulation (FiLM). These extra layers enable in-situ calibration of a trained model, allowing the user to configure the desired priority between robustness and clean-image performance after deployment.  However, these extra layers significantly increase training time, parameter count, and add latency which can prove costly  for  time  or  memory  constrained  applications.   In  this  paper,  we  present Fast Learnable Once-for-all Adversarial Training (FLOAT) which transforms the weight tensors without using extra layers, thereby incurring no significant increase in parameter count, training time, or network latency compared to a standard adversarial training. In particular, we add configurable scaled noise to the weight tensors that enables a ‘continuous’ trade-off between clean and adversarial performance.  Additionally, we extend FLOAT to slimmable neural networks to enable a three-way in-situ trade-off between robustness, accuracy, and complexity.  Extensive experiments show that FLOAT can yield state-of-the-art performance improving both clean and perturbed image classification by up to ∼6.5% and ∼14.5%,  respectively,  while  requiring  up  to 1.47x fewer  parameters with similar hyperparameter settings compared to FiLM-based alternatives.