Multi-Turn RL vs. Single-Turn Imitation Learning in VLA Navigation Sample Efficiency

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the sample efficiency of multi-turn RL training for VLA navigation compare to single-turn imitation learning as trajectory length increases. This paper develops LongNav-R1, an end-to-end multi-turn reinforcement learning (RL) framework designed to optimize Visual-Language-Action (VLA) models for long-horizon navigation. Unlike existing single-turn paradigm, LongNav-R1 reformulates the navigation decision process as a. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the sample efficiency of multi-turn RL training for VLA navigation compare to single-turn imitation learning as trajectory length increases?

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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