Iron: 意図整合と回顧的双方向学習による汎用仮想エージェントの性能向上フレームワーク
Iron: Intent-Aligned and Retrospective Dual Learning Framework for Enhancing Generalist Virtual Agents
GUIエージェントの訓練において、低レベル行動と高レベル意図の細粒度な整合を実現するステップワイズ循環整合報酬と、失敗軌跡を再利用する回顧的再現メカニズムを導入し、データ効率と性能を向上させた。
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著者: Jiahe Ying, Wendong Bu, Kaihang Pan, Bingchen Miao, Siyu Chen, Wen Wang, Xueming Jiang, Juncheng Li, Siliang Tang
分類: cs.CV
原文アブストラクト
Achieving virtual agents capable of automating tasks across diverse digital environments remains a pivotal challenge in Embodied AI. While Multimodal Large Language Models (MLLMs) offer enhanced visual perception and reasoning, their agentic deployment faces three challenges: costly data annotation, imprecise action-intent alignment, and inefficient exploration from discarded failed trajectories. To address these, we introduce Iron, an intent-aligned, self-improved, and annotation-efficient framework for training GUI agents. Iron employs a novel dual learning strategy that utilizes a stepwise cycle-consistent (SCC) reward to achieve fine-grained alignment between low-level actions and high-level intents, thereby improving instruction grounding and intent understanding. Concurrently, Iron introduces a hindsight reproduction mechanism to repurpose failed trajectories for training, improving both learning efficiency and task diversity. Extensive experiments demonstrate that Iron-trained generalist agents consistently improve performance on cross-environment and cross-device tasks, outperforming models trained with three times more data. Iron also achieves a substantial 25.06% relative improvement on unseen web tasks, with further gains observed on inherently complex tasks, demonstrating the feasibility of building more capable virtual agents.