Uses an intermediate teacher checkpoint to gate transfer of RL-induced policy shifts in on-policy distillation by checking directional agreement between early and late teacher changes; suppresses endpoint shifts that reverse and requires one extra frozen-teacher scoring pass.
Learns executable language-conditioned local navigation by pretraining on instruction-conditioned Internet egocentric videos, aligning learned visual dynamics with simulated action labels, then jointly fine-tuning for navigation. Achieves high success rates on short-horizon benchmarks (86.5% OVON, 94.9% HabitatGS-LN) with real-robot validation and released code/data.