AdaGuard uses an adaptive guard model to review agent trajectories against user-defined policies, with its 4B model reaching 89.30% binary accuracy on AdaptiveSafety
The work introduces AdaptiveSafety, a dataset of 10,939 training and 1,000 test examples covering policies with 1 to 100 rules, together with SafePO, a reinforcement learning algorithm, and uses them to train the AdaGuard family of 0.6B, 4B, and 8B guard models that assess agent trajectories under policies supplied at inference time; the 4B model achieves binary accuracies of 89.30% on AdaptiveSafety and 71.82% on DynaBench.