Skill-following agents execute complex tasks through step-by-step contracts
Beyond Instruction Following: Learning Grounded Skill-Following with Skill Contracts
Artificial Intelligence
Summary
Following instructions usually means just getting the final answer right, but the authors explore how to get computers to follow detailed step-by-step rules for tasks, like a recipe. They create a system called Grounded Skill-Following that breaks tasks into phases and checks each step is done correctly as the computer works. This system guides the computer throughout the task, giving feedback and rewards as it completes important steps in order. Their approach shows high rates of correctly completed procedures, even surpassing some original methods on task outcomes.
What this means in practice
- •For autonomous robotics teams: Enable robots to follow complex, multi-step protocols precisely by checking compliance with phase-specific skill contracts during execution.
- •For software automation engineers: Create AI agents that reliably execute and verify multi-phase procedures in software workflows by embedding explicit contract checks and milestone-based rewards.
Authors
Jianghan Shen, Zhenjie Liu, Yue Li, Jie Huang, Siqi Luo, Yiming Cheng, Yizhi Yao, Kaijie Zhang, Cheng Tang, Minghui Zhang, Ming Hu, Yirong Chen, Ziyan Huang
Abstract
Instruction following typically enforces discrete, response-level requirements, whereas an expert-authored skill prescribes procedural requirements spanning multiple phases and environment interactions. Given such a skill, we train the executor to execute all required phases instead of focusing solely on the final answer. We therefore introduce Grounded Skill-Following, which requires an agent to execute a fixed, expert-authored skill across its required phases by grounding decisions in environment observations. To achieve verifiable procedural execution, we formulate each skill as a skill contract combining visible skill instructions with an explicit contract runtime. The runtime specifies required phases, admissible actions, permitted transitions, and accepted termination. This structure provides a dense, verifiable training signal throughout execution. We leverage this by introducing Verified Progress Credit, which assigns rewards upon the initial completion of contract milestones and aggregates them into the trajectory return to guide policy optimization. During rollout, the contract runtime continuously tracks state transitions to provide Contract-State Feedback, which indicates whether the latest action is accepted and guides the agent toward valid next actions. To measure procedural compliance, we introduce the Protocol Completion Rate (PCR), defined as reaching accepted termination through all required phases, and decouple it from the final Task Outcome. Jointly trained with our framework, Qwen3.5-4B achieves Protocol Completion Rates of 99.27% on Math and 99.96% on Search, while slightly outperforming original baselines in Task Outcome (82.95% and 46.61%, respectively). Controlled studies examine how skill instructions, training signals, and contract-state feedback affect both metrics, while withholding interventions evaluate behavioral dependence on observation content.