Let's Reward Step-by-Step: Step-Aware Contrastive Alignment for Vision-Language Navigation in Continuous Environments

Vision-Language Navigation in Continuous Environments(VLN-CE) requires agents to learn complex reasoning from long-horizonhuman interactions. While multi-modal large language models (MLLMs)have driven recent progress, current training paradigms struggle to bal-ance generalization capability, error recovery, and training stability. Specif-ically, (i) policies derived from supervised fine-tuning (SFT) suffer fromcompounding errors, struggling to recover from out-of-distribution states,and (ii) Reinforcement fine-tuning (RFT) methods such as GRPO arebottlenecked by sparse outcome rewards. Their binary feedback fails toassign credit to individual steps, leading to gradient signal collapse infailure-dominant batches. To address these challenges, we introduce Step-Aware Contrastive Alignment (SACA), a framework designed to extractdense supervision from imperfect trajectories. At its core, the Perception-Grounded Step-Aware auditor evaluates progress step-by-step, disentan-gling failed trajectories into valid prefixes and exact divergence points.Leveraging these signals, the Scenario-Conditioned Group Constructionmechanism dynamically routes batches to specialized resampling andoptimization strategies. Extensive experiments on VLN-CE benchmarksdemonstrate that SACA achieves state-of-the-art performance.