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Learning How to Use Tools, Not Just When: Pattern-Aware Tool-Integrated Reasoning

Ningning Xu, Yuxuan Jiang, Shubhashis Roy Dipta, Hengyuan Zhang · Sep 27, 2025 · Citations: 0

Automatic Metrics Coding Math Pairwise Preference
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

High trust

Use this as a practical starting point for protocol research, then validate against the original paper.

Best use

Secondary protocol comparison source

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

High

Derived from extracted protocol signals and abstract evidence.

Abstract

Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems. Prior work has mainly studied when to invoke tools, while overlooking how tools are applied. We identify two common patterns: a calculator pattern that uses code for direct computation, and an algorithmic pattern that encodes problems as programs. Misaligned choices often cause failures even when reasoning is sound. We propose a two-stage framework that first builds code competence from both patterns and then aligns pattern selection with teacher preferences. Across challenging math datasets, our pattern-aware method substantially improves both code usage and accuracy, for instance raising Code@1 on MATH500 from 64.0% to 70.5% and on AIME24 from 26.7% to 50.0%. These gains highlight the effectiveness of a pattern-aware approach for tool-integrated reasoning.

Should You Rely On This Paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Best use

Secondary protocol comparison source

Use if you need

A benchmark-and-metrics comparison anchor.

Main weakness

No major weakness surfaced.

Trust level

High

Usefulness score

65/100 • Medium

Useful as a secondary reference; validate protocol details against neighboring papers.

Human Feedback Signal

Detected

Evaluation Signal

Detected

Usefulness for eval research

Moderate-confidence candidate

Extraction confidence 80%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Pairwise Preference

Directly usable for protocol triage.

"Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems."

Benchmarks / Datasets

strong

MATH 500

Useful for quick benchmark comparison.

"Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems."

Reported Metrics

strong

Accuracy

Useful for evaluation criteria comparison.

"Across challenging math datasets, our pattern-aware method substantially improves both code usage and accuracy, for instance raising Code@1 on MATH500 from 64.0% to 70.5% and on AIME24 from 26.7% to 50.0%."

Human Feedback Details

  • Uses human feedback: Yes
  • Feedback types: Pairwise Preference
  • Rater population: Not reported
  • Expertise required: Math, Coding

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: High
  • Use this page as: Secondary protocol comparison source

Protocol And Measurement Signals

Benchmarks / Datasets

MATH-500

Reported Metrics

accuracy

Research Brief

Metadata summary

Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems.
  • Prior work has mainly studied when to invoke tools, while overlooking how tools are applied.
  • We identify two common patterns: a calculator pattern that uses code for direct computation, and an algorithmic pattern that encodes problems as programs.

Researcher Actions

  • Compare this paper against others mentioning MATH.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

MATH Accuracy

Research Summary

Contribution Summary

  • We propose a two-stage framework that first builds code competence from both patterns and then aligns pattern selection with teacher preferences.
  • Across challenging math datasets, our pattern-aware method substantially improves both code usage and accuracy, for instance raising Code@1 on MATH500 from 64.0% to 70.5% and on AIME24 from 26.7% to 50.0%.

Why It Matters For Eval

  • We propose a two-stage framework that first builds code competence from both patterns and then aligns pattern selection with teacher preferences.

Researcher Checklist

  • Pass: Human feedback protocol is explicit

    Detected: Pairwise Preference

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: MATH-500

  • Pass: Metric reporting is present

    Detected: accuracy

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