Capability guides environment design for better multi-step agent tasks
Skill2Env: Capability-Oriented Environment Synthesis from Skills for General Agents
Artificial Intelligence
Summary
Creating detailed environments where AI agents can practice complex tasks is very hard. The authors designed Skill2Env, a method that starts with a basic skill and builds up a full task environment by adding challenges based on what the agent can do. They also created a way to gradually make these tasks harder if they’re too easy. Training AI on tasks from these environments helps them perform better on many different challenges.
What this means in practice
- •For ai developers: Generate tailored training environments that adapt in difficulty to improve AI agents’ multi-step interaction skills.
- •For game designers: Create dynamic challenge settings in games where tasks evolve in complexity based on player or AI skill levels.
Authors
Weiyi Xu, Xiaowen Yang, Wen Da, Hang Xu, Canwei Li, Hongjie You, Pusen Dong, Yucheng Zeng, Zhaokai Luo, Mu Chuan
Abstract
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.