  Vision-and-Language Navigation (VLN) is a challenging task in the field of
artificial intelligence. Although massive progress has been made in this task
over the past few years attributed to breakthroughs in deep vision and language
models, it remains tough to build VLN models that can generalize as well as
humans. In this paper, we provide a new perspective to improve VLN models.
Based on our discovery that snapshots of the same VLN model behave
significantly differently even when their success rates are relatively the
same, we propose a snapshot-based ensemble solution that leverages predictions
among multiple snapshots. Constructed on the snapshots of the existing
state-of-the-art (SOTA) model $\circlearrowright$BERT and our past-action-aware
modification, our proposed ensemble achieves the new SOTA performance in the
R2R dataset challenge in Navigation Error (NE) and Success weighted by Path
Length (SPL).
