Multilingual VLN Agents on RxR and R2R: Cross-Lingual Transfer Learning Performance

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: Do multilingual VLN agents trained on RxR demonstrate improved cross-lingual transfer learning capabilities when evaluated on the Room-to-Region (R2R) dataset for English and non-English instructions. Vision-and-Language Navigation (VLN) tasks require an agent to navigate through the environment based on language instructions. In this paper, we aim to solve two key challenges in this task: utilizing multilingual instructions for improved instruction-path grounding and. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Do multilingual VLN agents trained on RxR demonstrate improved cross-lingual transfer learning capabilities when evaluated on the Room-to-Region (R2R) dataset for English and non-English instructions?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance