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

Francis Ferraro; Yuxuan Jiang

arxiv: 2601.03555 · v2 · submitted 2026-01-07 · 💻 cs.AI

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

Yuxuan Jiang , Francis Ferraro This is my paper

Pith reviewed 2026-05-16 17:19 UTC · model grok-4.3

classification 💻 cs.AI

keywords tool-using language modelsreinforcement learningreward modelingskill prototypesmid-level supervisioncredit assignmentmulti-step reasoningtool-augmented agents

0 comments

The pith

SCRIBE uses skill prototypes to supply structured rubrics that reduce reward variance in multi-step tool use by language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a reinforcement learning framework that intervenes at mid-level abstraction rather than relying solely on process-level or outcome-level signals. It curates a library of skill prototypes and routes each subgoal to the matching prototype so the reward model receives precise, task-specific rubrics instead of open-ended judgments. This structured verification is intended to lower variance in credit assignment during long reasoning chains. A sympathetic reader would care because noisy rewards have been a persistent barrier to training reliable tool-augmented agents that handle complex, multi-turn interactions.

Core claim

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 receives precise, structured rubrics that substantially reduce reward variance. The approach produces state-of-the-art results on reasoning and tool-use benchmarks, including raising AIME25 accuracy of a Qwen3-4B model from 43.3 percent to 63.3 percent, and markedly higher success rates in complex multi-turn tool interactions. Training dynamics reveal co-evolution across abstraction levels, with mid-level skill mastery preceding effective high-level planning, and the mid-

What carries the argument

The library of skill prototypes together with the routing mechanism that conditions the reward model on intermediate behavioral evaluations at the subgoal level.

Load-bearing premise

That matching each subgoal to a skill prototype supplies rubrics precise enough to substantially reduce reward variance in the LLM judge.

What would settle it

A controlled experiment showing no measurable drop in reward variance and no benchmark gains when the prototype routing is added to an otherwise identical training run would falsify the central mechanism.

Figures

Figures reproduced from arXiv: 2601.03555 by Francis Ferraro, Yuxuan Jiang.

**Figure 2.** Figure 2: A compact illustration of a skill prototype [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Structural training dynamics of our method. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SCRIBE adds skill prototypes for mid-level reward structure in tool agents and shows clear benchmark gains, but the variance reduction claim rests on unablated assertions.

read the letter

The main thing to know is that SCRIBE routes subgoals to a library of skill prototypes so the reward model gets concrete rubrics instead of open-ended LLM judgments, and the reported results include a 20-point AIME25 lift for Qwen3-4B plus better multi-turn tool success rates. The training-dynamics section also notes that mid-level skill mastery tends to appear before high-level planning improves. Those pieces are the concrete contributions. The framework description is straightforward and the idea of turning evaluation into constrained verification at an intermediate abstraction level is a reasonable intervention. The numbers on the benchmarks are large enough to notice. The soft spot is exactly where the stress test points: there is no direct measurement of reward variance before and after the prototype step, no ablation that removes the routing while keeping supervision density fixed, and no error bars or statistical checks on the gains. Without those, the improvements could come from simply having more structured feedback overall rather than from the mid-level prototypes themselves. The abstract does not show the full experimental controls, so the central mechanism is asserted more than demonstrated. This paper is aimed at people working on process rewards and tool-augmented agents. A reader who wants practical ideas for structuring supervision in multi-step tasks will get something from the framework and the numbers. It is coherent on its own terms and shows honest engagement with the credit-assignment problem, so it deserves a serious referee to check the implementation details and run the missing ablations.

Referee Report

2 major / 2 minor

Summary. The paper introduces SCRIBE, a reinforcement learning framework for tool-augmented language models that grounds reward modeling in a curated library of skill prototypes. By routing each subgoal to a matching prototype, open-ended LLM evaluation is converted into constrained verification, which the authors claim substantially reduces reward variance and improves credit assignment in multi-step reasoning. Experimental results report state-of-the-art performance on reasoning and tool-use benchmarks, including an increase in AIME25 accuracy for Qwen3-4B from 43.3% to 63.3%, higher success rates in complex multi-turn tool interactions, and a co-evolution dynamic in which mid-level skill mastery precedes effective high-level planning; the method is also shown to be additive to low-level tool optimizations.

Significance. If the variance-reduction mechanism is validated, SCRIBE offers a scalable mid-level abstraction that complements existing low-level optimizations and addresses a core challenge in training reliable tool-using agents. The reported benchmark gains and training-dynamics observations would constitute a meaningful contribution to process supervision and hierarchical skill acquisition in LLM agents.

major comments (2)

[Section 3] Framework description: the central assertion that routing subgoals to skill prototypes equips the reward model with precise rubrics that 'substantially reduce reward variance' is load-bearing for the contribution, yet the manuscript provides no direct quantitative support such as pre/post variance statistics, reward-distribution comparisons, or isolating ablations that hold other RL components fixed.
[Section 4] Experimental results: the reported 20-point AIME25 lift and multi-turn gains are presented without error bars, statistical significance tests, or ablations that strip the prototype-routing component while controlling for supervision density and compute; this leaves open the possibility that gains arise from generic increases in training signal rather than the mid-level abstraction.

minor comments (2)

[Abstract] The abstract states that success rates in multi-turn tool interactions increase 'significantly' but supplies no concrete percentages or baseline comparisons.
[Section 5] The training-dynamics analysis of co-evolution across abstraction levels would be strengthened by explicit metrics or figures quantifying the temporal precedence of mid-level mastery.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which identifies key areas where additional empirical support would strengthen the paper. We address each major comment below and will incorporate the requested analyses in the revised manuscript.

read point-by-point responses

Referee: [Section 3] Framework description: the central assertion that routing subgoals to skill prototypes equips the reward model with precise rubrics that 'substantially reduce reward variance' is load-bearing for the contribution, yet the manuscript provides no direct quantitative support such as pre/post variance statistics, reward-distribution comparisons, or isolating ablations that hold other RL components fixed.

Authors: We agree that direct quantitative evidence for variance reduction is currently absent and would strengthen the central claim. In the revision we will add pre/post reward variance statistics, reward-distribution comparisons, and an isolating ablation that holds all other RL components fixed while varying only the prototype-routing mechanism. revision: yes
Referee: [Section 4] Experimental results: the reported 20-point AIME25 lift and multi-turn gains are presented without error bars, statistical significance tests, or ablations that strip the prototype-routing component while controlling for supervision density and compute; this leaves open the possibility that gains arise from generic increases in training signal rather than the mid-level abstraction.

Authors: We acknowledge the need for greater statistical rigor and targeted controls. The revised manuscript will include error bars across multiple random seeds, statistical significance tests, and ablations that isolate the prototype-routing component while matching supervision density and compute budget, to clarify whether gains derive from the mid-level abstraction. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework without self-referential derivations or fitted predictions

full rationale

The paper presents SCRIBE as an RL intervention that routes subgoals to skill prototypes to convert open-ended LLM judgment into constrained verification. No equations, derivations, or first-principles predictions appear in the abstract or described text. The central claim of reduced reward variance is offered as a qualitative mechanism whose support is the reported benchmark gains (e.g., AIME25 lift), not a mathematical reduction to fitted inputs or self-citations. No self-definitional loops, uniqueness theorems, or ansatz smuggling are present; the work is self-contained as an empirical method whose validity rests on external benchmark results rather than internal redefinition.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the existence and effectiveness of a curated library of skill prototypes that can be reliably matched to subgoals; this library is introduced without independent evidence of its construction or coverage in the abstract.

axioms (1)

domain assumption LLM-based judges produce noisy signals without fine-grained task-specific rubrics
Invoked to motivate the need for structured prototypes.

invented entities (1)

skill prototypes no independent evidence
purpose: To supply precise, structured rubrics for mid-level reward modeling
New postulated library that transforms open-ended evaluation into constrained verification

pith-pipeline@v0.9.0 · 5545 in / 1326 out tokens · 52507 ms · 2026-05-16T17:19:07.871511+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

By routing each subgoal to a corresponding prototype, the reward model is equipped with precise, structured rubrics that substantially reduce reward variance.
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

mid-level skill mastery serves as a precursor to the emergence of strategic high-level planning

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matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

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From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models
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A survey of credit assignment techniques in LLM reinforcement learning that distinguishes maturing methods for reasoning from new approaches needed for agentic settings and provides supporting resources.
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Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · cited by 5 Pith papers · 3 internal anchors

[1]

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Tora: A tool-integrated reasoning agent for mathematical problem solving. InThe Twelfth Inter- national Conference on Learning Representations. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shi- rong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcemen...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[2]

Reasoning through exploration: A reinforcement learning framework for robust function calling.arXiv preprint arXiv:2508.05118, 2025

Reasoning through exploration: A reinforce- ment learning framework for robust function calling. arXiv preprint arXiv:2508.05118. Yuxuan Jiang and Francis Ferraro. 2024. Memo- rization over reasoning? exposing and mitigating verbatim memorization in large language models’ character understanding evaluation.arXiv preprint arXiv:2412.14368. Yuxuan Jiang, Da...

work page arXiv 2024
[3]

InThe Twelfth Inter- national Conference on Learning Representations

Let’s verify step by step. InThe Twelfth Inter- national Conference on Learning Representations. Zihan Lin, Xiaohan Wang, Jie Cao, Jiajun Chai, Guojun Yin, Wei Lin, and Ran He. 2025. Rest: Reshaping token-level policy gradients for tool-use large lan- guage models.arXiv preprint arXiv:2509.21826. Weiwen Liu, Xu Huang, Xingshan Zeng, Xinlong Hao, Shuai Yu,...

work page arXiv 2025
[4]

Understanding r1-zero-like training: A critical perspective.arXiv preprint arXiv:2503.20783. OpenAI. 2024. Openai api documentation. https:// platform.openai.com/docs. Accessed via official OpenAI API. Shishir G Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E Gonzalez. 2025. The berkeley function calling leade...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[5]

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Rewarding progress: Scaling automated pro- cess verifiers for llm reasoning.arXiv preprint arXiv:2410.08146. Lucy Xiaoyang Shi, Joseph J Lim, and Youngwoon Lee. 2022. Skill-based model-based reinforcement learning.arXiv preprint arXiv:2207.07560. Fengwei Teng, Zhaoyang Yu, Quan Shi, Jiayi Zhang, Chenglin Wu, and Yuyu Luo. 2025. Atom of thoughts for markov...

work page internal anchor Pith review arXiv 2022
[6]

SimpleTIR: End-to-end reinforcementlearningformulti-turntool-integratedreasoning.arXivpreprintarXiv:2509.02479,2025

Learning how to use tools, not just when: Pattern-aware tool-integrated reasoning. InThe 5th Workshop on Mathematical Reasoning and AI at NeurIPS 2025. Zhenghai Xue, Longtao Zheng, Qian Liu, Yingru Li, Xiaosen Zheng, Zejun Ma, and Bo An. 2025. Simpletir: End-to-end reinforcement learning for multi-turn tool-integrated reasoning.arXiv preprint arXiv:2509.0...

work page arXiv 2025
[7]

Its current value is 34479.60 with a percentage change of +0.47%

**DOW J**: The Dow Jones Industrial Average is a price-weighted average of 30 blue-chip stocks that are generally the leaders in their industry. Its current value is 34479.60 with a percentage change of +0.47%

work page
[8]

Its current value is 13691.30 with a percentage change of +0.90%.This information can help you make informed decisions about your investment plans

**NASDAQ**: The NASDAQ Composite is a broad-based capitalization-weighted index of stocks in all three NASDAQ tiers: Global Select, Global Market and Capital Market. Its current value is 13691.30 with a percentage change of +0.90%.This information can help you make informed decisions about your investment plans. E Execution-Verified Plan Selection and Str...

work page
[9]

Identify relevant intermediate results (bounds, constraints) that the conclusion must follow from

work page
[10]

max equals bound

Determine if the subgoal requires an implication or a tightness claim (e.g., "max equals bound")

work page
[11]

If concluding an extremum, checktightness: reference a construction or equality condition

work page
[12]

Verify domain restrictions and case completeness (e.g., integer ranges, geometric feasibility)

work page
[13]

forn≥5,6n≤2 n (impossible)

State the final conclusion succinctly (value, choice, or maximal attainable level). Judging Rubric (0–3 Step Score): • 3 (Correct & Complete):Conclusion follows rigorously from prior results; addresses tight- ness/feasibility. • 2 (Minor Flaw):Correct strategy and conclusion, but with minor slips (arithmetic/wording) or obvious-but-unstated justifications...

work page
[14]

Goal & Constraints Identification:Restate the user intent and identify required inputs (entity IDs, time range, text to analyze) and constraints (access, tool availability, policy limits)

work page
[15]

Tool Selection & Invocation:Choose the correct tool and issue a well-formed call with task-relevant parameters (e.g.,shareuid,from/to;text=)

work page
[16]

Structured Output Interpretation:Parse returned fields and map them to the user request (e.g.,roa_ratio→ROA for FY2025; sentiment label + keywords)

work page
[17]

2025-01-01

Limitation Detection & Fallback:If the request cannot be completed (missing access/data/- tool), explicitly state the limitation, offer concrete next steps (how to obtain the data / what to provide), and propose safe alternatives. Judging Rubric (0–3 Step Score): •3 (Correct & Complete):Correctly identifies constraints...(ommitted) • 2 (Minor Flaw):Overal...

work page 2025

[1] [1]

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Tora: A tool-integrated reasoning agent for mathematical problem solving. InThe Twelfth Inter- national Conference on Learning Representations. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shi- rong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcemen...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[2] [2]

Reasoning through exploration: A reinforcement learning framework for robust function calling.arXiv preprint arXiv:2508.05118, 2025

Reasoning through exploration: A reinforce- ment learning framework for robust function calling. arXiv preprint arXiv:2508.05118. Yuxuan Jiang and Francis Ferraro. 2024. Memo- rization over reasoning? exposing and mitigating verbatim memorization in large language models’ character understanding evaluation.arXiv preprint arXiv:2412.14368. Yuxuan Jiang, Da...

work page arXiv 2024

[3] [3]

InThe Twelfth Inter- national Conference on Learning Representations

Let’s verify step by step. InThe Twelfth Inter- national Conference on Learning Representations. Zihan Lin, Xiaohan Wang, Jie Cao, Jiajun Chai, Guojun Yin, Wei Lin, and Ran He. 2025. Rest: Reshaping token-level policy gradients for tool-use large lan- guage models.arXiv preprint arXiv:2509.21826. Weiwen Liu, Xu Huang, Xingshan Zeng, Xinlong Hao, Shuai Yu,...

work page arXiv 2025

[4] [4]

Understanding r1-zero-like training: A critical perspective.arXiv preprint arXiv:2503.20783. OpenAI. 2024. Openai api documentation. https:// platform.openai.com/docs. Accessed via official OpenAI API. Shishir G Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E Gonzalez. 2025. The berkeley function calling leade...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[5] [5]

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Rewarding progress: Scaling automated pro- cess verifiers for llm reasoning.arXiv preprint arXiv:2410.08146. Lucy Xiaoyang Shi, Joseph J Lim, and Youngwoon Lee. 2022. Skill-based model-based reinforcement learning.arXiv preprint arXiv:2207.07560. Fengwei Teng, Zhaoyang Yu, Quan Shi, Jiayi Zhang, Chenglin Wu, and Yuyu Luo. 2025. Atom of thoughts for markov...

work page internal anchor Pith review arXiv 2022

[6] [6]

SimpleTIR: End-to-end reinforcementlearningformulti-turntool-integratedreasoning.arXivpreprintarXiv:2509.02479,2025

Learning how to use tools, not just when: Pattern-aware tool-integrated reasoning. InThe 5th Workshop on Mathematical Reasoning and AI at NeurIPS 2025. Zhenghai Xue, Longtao Zheng, Qian Liu, Yingru Li, Xiaosen Zheng, Zejun Ma, and Bo An. 2025. Simpletir: End-to-end reinforcement learning for multi-turn tool-integrated reasoning.arXiv preprint arXiv:2509.0...

work page arXiv 2025

[7] [7]

Its current value is 34479.60 with a percentage change of +0.47%

**DOW J**: The Dow Jones Industrial Average is a price-weighted average of 30 blue-chip stocks that are generally the leaders in their industry. Its current value is 34479.60 with a percentage change of +0.47%

work page

[8] [8]

Its current value is 13691.30 with a percentage change of +0.90%.This information can help you make informed decisions about your investment plans

**NASDAQ**: The NASDAQ Composite is a broad-based capitalization-weighted index of stocks in all three NASDAQ tiers: Global Select, Global Market and Capital Market. Its current value is 13691.30 with a percentage change of +0.90%.This information can help you make informed decisions about your investment plans. E Execution-Verified Plan Selection and Str...

work page

[9] [9]

Identify relevant intermediate results (bounds, constraints) that the conclusion must follow from

work page

[10] [10]

max equals bound

Determine if the subgoal requires an implication or a tightness claim (e.g., "max equals bound")

work page

[11] [11]

If concluding an extremum, checktightness: reference a construction or equality condition

work page

[12] [12]

Verify domain restrictions and case completeness (e.g., integer ranges, geometric feasibility)

work page

[13] [13]

forn≥5,6n≤2 n (impossible)

State the final conclusion succinctly (value, choice, or maximal attainable level). Judging Rubric (0–3 Step Score): • 3 (Correct & Complete):Conclusion follows rigorously from prior results; addresses tight- ness/feasibility. • 2 (Minor Flaw):Correct strategy and conclusion, but with minor slips (arithmetic/wording) or obvious-but-unstated justifications...

work page

[14] [14]

Goal & Constraints Identification:Restate the user intent and identify required inputs (entity IDs, time range, text to analyze) and constraints (access, tool availability, policy limits)

work page

[15] [15]

Tool Selection & Invocation:Choose the correct tool and issue a well-formed call with task-relevant parameters (e.g.,shareuid,from/to;text=)

work page

[16] [16]

Structured Output Interpretation:Parse returned fields and map them to the user request (e.g.,roa_ratio→ROA for FY2025; sentiment label + keywords)

work page

[17] [17]

2025-01-01

Limitation Detection & Fallback:If the request cannot be completed (missing access/data/- tool), explicitly state the limitation, offer concrete next steps (how to obtain the data / what to provide), and propose safe alternatives. Judging Rubric (0–3 Step Score): •3 (Correct & Complete):Correctly identifies constraints...(ommitted) • 2 (Minor Flaw):Overal...

work page 2025