Reflection-Based Task Adaptation for Self-Improving VLA

2025-10-14

AI Summary

To address the challenge of efficiently and unsupervisedly adapting vision-language-action (VLA) models to novel tasks in real-world settings, this paper proposes a reflection-driven dual-path adaptation framework. The method integrates failure-driven reflective reinforcement learning with success-driven quality-guided supervised fine-tuning. It employs causal reasoning to construct dense reward functions, selective trajectory imitation, and conditional curriculum learning to enable autonomous, iterative policy optimization. Unlike conventional RL approaches, the framework significantly mitigates reward hacking, ensuring goal alignment and behavioral robustness. Empirical evaluation on complex manipulation tasks demonstrates a 37% acceleration in convergence speed and an average 22.6% improvement in final success rate. These results validate the framework’s comprehensive advantages in adaptation efficiency, training stability, and cross-task generalization.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Computer Vision: Language and Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Pre-trained Vision-Language-Action (VLA) models represent a major leap towards general-purpose robots, yet efficiently adapting them to novel, specific tasks in-situ remains a significant hurdle. While reinforcement learning (RL) is a promising avenue for such adaptation, the process often suffers from low efficiency, hindering rapid task mastery. We introduce Reflective Self-Adaptation, a framework for rapid, autonomous task adaptation without human intervention. Our framework establishes a self-improving loop where the agent learns from its own experience to enhance both strategy and execution. The core of our framework is a dual-pathway architecture that addresses the full adaptation lifecycle. First, a Failure-Driven Reflective RL pathway enables rapid learning by using the VLM's causal reasoning to automatically synthesize a targeted, dense reward function from failure analysis. This provides a focused learning signal that significantly accelerates policy exploration. However, optimizing such proxy rewards introduces a potential risk of"reward hacking,"where the agent masters the reward function but fails the actual task. To counteract this, our second pathway, Success-Driven Quality-Guided SFT, grounds the policy in holistic success. It identifies and selectively imitates high-quality successful trajectories, ensuring the agent remains aligned with the ultimate task goal. This pathway is strengthened by a conditional curriculum mechanism to aid initial exploration. We conduct experiments in challenging manipulation tasks. The results demonstrate that our framework achieves faster convergence and higher final success rates compared to representative baselines. Our work presents a robust solution for creating self-improving agents that can efficiently and reliably adapt to new environments.

Problem

• Adapting pre-trained VLA models to novel tasks efficiently
• Overcoming low efficiency in reinforcement learning for task adaptation
• Preventing reward hacking while accelerating autonomous policy learning

Innovation

• Dual-pathway architecture for full adaptation lifecycle
• Failure-driven reflective RL with dense reward synthesis
• Success-driven quality-guided SFT with conditional curriculum