Path-level Hindsight Instructions for Semantic Exploration in Vision-Language Navigation

On-policy exploration is a crucial component for training robust Vision-Language Navigation agents, as it exposes the policy to a broader state distribution. However, such exploration inevitably leads to trajectories that deviate from expert demonstrations, resulting in a semantic mismatch between the executed visual stream and the original language instruction. In this work, we address this challenge by introducing Phi-Nav, a unified on-policy framework that leverages hindsight reasoning to ali

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ethics.ai (2 July 2026), “Path-level Hindsight Instructions for Semantic Exploration in Vision-Language Navigation,” evidence record 331, https://ethics.ai/record/331 (originally published by arXiv).

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