AI RESEARCH
The Essence of Balance for Self-Improving Agents in Vision-and-Language Navigation
arXiv CS.CV
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ArXi:2604.19064v1 Announce Type: new In vision-and-language navigation (VLN), self-improvement from policy-induced experience, using only standard VLN action supervision, critically depends on balancing behavioral diversity and learning stability, which governs whether the agent can extract a reliable learning signal for improvement.