RecoverFly: A Failure-Aware Reinforcement Learning Post-Training Framework for Aerial Vision-Language Navigation
RecoverFly is proposed, a failure-aware RL post-training framework for end-to-end UAV-VLA policies that adapts token-level RL for stable optimization of grammar-constrained autoregressive UAV actions, revisits unresolved failure cases to strengthen corrective learning and sample utilization, and combines a two-stage long-tail scene curriculum with reference-policy regularization to improve scene adaptation while preserving acquired capabilities.
Boxiong Wang, Hui Kang, Geng Sun et al.
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