LongNav-R1 Efficiency Gains Over Single-Turn VLA Policies on RxR-CE

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: What is the efficiency gain of LongNav-R1 compared to single-turn VLA policies in terms of inference time and compute resources on the RxR-CE benchmark. Embodied AI is widely recognized as a cornerstone of artificial general intelligence (AGI) because it involves controlling embodied agents to perform tasks in the physical world. Building on the success of large language models (LLMs) and vision-language models (VLMs), a new. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the efficiency gain of LongNav-R1 compared to single-turn VLA policies in terms of inference time and compute resources on the RxR-CE benchmark?

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