Search papers, labs, and topics across Lattice.
Affiliation:
2
0
2
Moving quality-diversity optimization from raw policy parameters into the latent space of a behavioral foundation model rescues evolutionary search from total collapse in sparse, deceptive environments while eliminating critic training entirely.
Randomly sampling tasks in offline RL hurts zero-shot generalization, but extracting task vectors directly from the dataset boosts performance by 20%.
