ToolAnchor：锚定反事实上下文以提升智能体工具使用能力

中文摘要 AI 辅助

工具增强的大语言模型智能体在长期任务中表现出色，但通常在固定工具集上进行后训练。当任务需要新工具时，它们难以有效纳入，从头重新训练往往不切实际。我们将工具集扩展问题的核心障碍识别为行为惯性。我们证明在关键决策点注入反事实锚定上下文可打破这种惯性。为此提出ToolAnchor框架，通过教师模型假设反事实上下文，经学生展开验证并通过智能体后训练内化成功干预。广泛评估表明ToolAnchor在扩展工具集下性能出色，弥合了静态后训练与动态适应的差距，为可扩展的智能体强化学习开辟新路径。

英文摘要

Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets. When tasks demand new tools, these agents struggle to incorporate them effectively, and retraining from scratch is often impractical. We identify the core obstacle in such toolset expansion problem as behavioral inertia: the tendency of agents to fall back on familiar tools and established reasoning patterns despite having access to new ones. We demonstrate that injecting counterfactual anchor contexts at critical decision points can break this inertia, recovering failed trajectories by eliciting suppressed agent capabilities. To scale this insight, we propose ToolAnchor, a framework that uses teacher models to hypothesize these counterfactual contexts, verifies them via student rollouts, and internalizes the successful interventions through agentic post-training. Extensive evaluations across general AI assistant (GAIA), textual search (BrowseComp), and visual search (VDR-Bench) tasks demonstrate that ToolAnchor consistently exhibits competitive performance under expanded toolsets. Our work bridges the gap between static post-training and dynamic adaptation, charting a new path for scalable agentic reinforcement learning.

发表机构

• Fudan University（复旦大学）
• Nanyang Technological University（南洋理工大学）
• MIT（麻省理工学院）

机构由 AI 辅助整理，请以论文原文为准。