Scaling Laws of 7B and 13B VLA Models in LongNav-R1 on R2R-CE

This report synthesises findings from 2 peer-reviewed papers addressing the following research question: Does the inference efficiency (latency/throughput) of 7B and 13B VLA models scale linearly with instruction complexity in LongNav-R1 on R2R-CE, and how does this correlate with their grounding and. The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: Does the inference efficiency (latency/throughput) of 7B and 13B VLA models scale linearly with instruction complexity in LongNav-R1 on R2R-CE, and how does this correlate with their grounding and path completion performance?

Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.