Horizon-Adaptive Multi-Turn Reinforcement Learning for Robust VLA Models in ALFRED

This report synthesises findings from 4 peer-reviewed papers addressing the following research question: Does horizon-adaptive multi-turn RL improve the robustness of VLA models to environmental perturbations and instruction ambiguity in the ALFRED benchmark relative to supervised single-turn approaches. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: Does horizon-adaptive multi-turn RL improve the robustness of VLA models to environmental perturbations and instruction ambiguity in the ALFRED benchmark relative to supervised single-turn approaches?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.