Skill2Env：面向通用智能体的基于技能的能力导向环境合成

中文摘要 AI 辅助

可执行环境对于在需要工具使用和多步交互的任务上对智能体进行后训练至关重要，但构建可执行任务及其环境仍然难以规模化。技能提供了可复用的领域知识、操作流程和工具使用说明，但技能中包含的信息与一个具有完整可执行环境的具体且具有挑战性的任务之间仍存在巨大差距。为解决这一差距，我们引入了Skill2Env，一个能力导向的框架，该框架从技能出发，利用智能体的能力需求来指导任务和环境的合成。Skill2Env通过可复用的难度模式来表示这些需求，并将其实例化为指定目标、挑战、环境事实、信息边界和验收标准的任务蓝图。这些蓝图指导围绕源技能的任务指令、执行基质、工作空间和基于评分标准的评估器的联合构建。我们进一步提出了迭代任务强化，该方法利用求解器执行证据来识别挑战性不足的任务设计，加强或扩展其难度模式实例化，并修订相应的蓝图和环境。使用从Skill2Env环境生成的1.5K个高得分轨迹进行监督微调，我们在广泛的智能体基准上观察到一致的改进，证明了能力导向的环境合成对智能体后训练的有效性。

英文摘要

Executable environments are critical for post-training agents on tasks that require tool use and multi-step interaction, but constructing executable tasks together with their environments remains difficult to scale. Skills provide reusable domain knowledge, operational procedures, and tool-use instructions, but a substantial gap remains between the information contained in a skill and a concrete, challenging task with a complete executable environment. To address this gap, we introduce Skill2Env, a capability-oriented framework that starts from a skill and uses agent capability demands to guide task and environment synthesis. Skill2Env represents these demands through reusable difficulty patterns and instantiates them into task blueprints that specify objectives, challenges, environment facts, information boundaries, and acceptance criteria. These blueprints guide the joint construction of task instructions, execution substrates, workspaces, and rubric-based evaluators around source skills. We further propose Iterative Task Hardening, which uses solver execution evidence to identify insufficiently challenging task designs, strengthen or extend their difficulty-pattern instantiations, and revise the corresponding blueprints and environments. Using 1.5K high-scoring trajectories generated from Skill2Env environments for supervised fine-tuning, we observe consistent improvements across a broad range of agent benchmarks, demonstrating the effectiveness of capability-oriented environment synthesis for agent post-training.

发表机构

• AllSpark Team（AllSpark团队）

机构由 AI 辅助整理，请以论文原文为准。