VLN-R1 shows that a Qwen2-VL model trained with supervised fine-tuning plus GRPO-based reinforcement fine-tuning can navigate continuous indoor environments from egocentric video, improving success rate on VLN-CE R2R and RxR benchmarks over its own SFT baselines.
Matterport3D: Learning from RGB-D data in indoor environments
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VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning
VLN-R1 shows that a Qwen2-VL model trained with supervised fine-tuning plus GRPO-based reinforcement fine-tuning can navigate continuous indoor environments from egocentric video, improving success rate on VLN-CE R2R and RxR benchmarks over its own SFT baselines.