Skill2Env: Capability-Oriented Environment Synthesis from Skills for General Agents
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Abstract (arXiv)
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.
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References · 20
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
- 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
- 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
- 2024Training Software Engineering Agents and Verifiers with SWE-Gymcited by 300
- 2025SWE-smith: Scaling Data for Software Engineering Agentscited by 255
- 2026Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forwardcited by 105
- 2026Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agentscited by 57
- 2025VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applicationscited by 45
- 2026Endless Terminals: Scaling RL Environments for Terminal Agentscited by 43
- 2026τ-Knowledge: Evaluating Conversational Agents over Unstructured Knowledgecited by 35
8 more
- 2026CLI-Gym: Scalable CLI Task Generation via Agentic Environment Inversioncited by 20
- 2026Toward Scalable Terminal Task Synthesis via Skill Graphscited by 16
- 2026Terminal-World: Scaling Terminal-Agent Environments via Agent Skillscited by 13
- 2026CLI-Universe: Towards Verifiable Task Synthesis Engine for Terminal Agentscited by 7
- 2026NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMscited by 4
- 2026FACET: Preserving Source Intent and Executable State in Terminal Task Synthesiscited by 3
- 2026Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environmentscited by 3
- 2026SKT: Skill-Use Training at Scale via Verified Synthetic Data Generationcited by 2
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