SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models

• URL: http://arxiv.org/abs/2601.03555v1
• Date: Wed, 07 Jan 2026 03:49:48 GMT
• Title: SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models
• Authors: Yuxuan Jiang, Francis Ferraro,
• Abstract summary: SCRIBE is a reinforcement learning framework that intervenes at a novel mid-level abstraction.<n>It achieves state-of-the-art performance across a range of reasoning and tool-use benchmarks.
• Score: 10.04930078540686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning. While process-level reward models offer a promising direction, existing LLM-based judges often produce noisy and inconsistent signals because they lack fine-grained, task-specific rubrics to distinguish high-level planning from low-level execution. In this work, we introduce SCRIBE (Skill-Conditioned Reward with Intermediate Behavioral Evaluation), a reinforcement learning framework that intervenes at a novel mid-level abstraction. SCRIBE grounds reward modeling in a curated library of skill prototypes, transforming open-ended LLM evaluation into a constrained verification problem. By routing each subgoal to a corresponding prototype, the reward model is equipped with precise, structured rubrics that substantially reduce reward variance. Experimental results show that SCRIBE achieves state-of-the-art performance across a range of reasoning and tool-use benchmarks. In particular, it improves the AIME25 accuracy of a Qwen3-4B model from 43.3% to 63.3%, and significantly increases success rates in complex multi-turn tool interactions. Further analysis of training dynamics reveals a co-evolution across abstraction levels, where mastery of mid-level skills consistently precedes the emergence of effective high-level planning behaviors. Finally, we demonstrate that SCRIBE is additive to low-level tool optimizations, providing a scalable and complementary pathway toward more autonomous and reliable tool-using agents.

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