mlsanityData to 14 Oct 2026

Beyond Instruction Following: Learning Grounded Skill-Following with Skill Contracts

arXiv 2610.05161 ↗First posted 4 Oct 2026cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 51 of its 58 references that are in the map. First posted on arXiv 4 Oct 2026.

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References · 51

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38 more
  1. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
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  22. 2026Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agentscited by 90
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  32. 2026SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignmentcited by 9
  33. 2026SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Usecited by 8
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  37. 2026Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agentscited by 4
  38. 2026SKT: Skill-Use Training at Scale via Verified Synthetic Data Generationcited by 3

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