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arxiv: 2605.06326 · v1 · submitted 2026-05-07 · 💻 cs.CL

Recognition: unknown

Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning

Bowen Zhou, Ganqu Cui, Ning Ding, Qianjia Cheng, Shunkai Zhang, Yuchen Fan, Yu Cheng, Yuchen Zhang, Yun Luo, Yu Qiao, Yuxin Zuo, Zhilin Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-08 10:23 UTC · model grok-4.3

classification 💻 cs.CL
keywords tool-integrated reasoningthinking modelssupervised fine-tuningreinforcement learningtool usecatastrophic forgettingAIME benchmarkopen-source models
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The pith

A full training recipe lets strong thinking models adopt tool use without losing their text-only reasoning strengths.

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

The paper aims to show how to integrate tool calling into advanced thinking models while preserving their ability to reason purely with text. It identifies key issues like degraded performance from tool evaluation and proposes a pipeline that selects suitable training examples, balances tool and non-tool data, optimizes for specific metrics, and applies reinforcement learning. A sympathetic reader would care because this extends model capabilities beyond current limits on complex problems without the common trade-off of forgetting prior skills. The approach yields top results on math and other benchmarks for both small and large models.

Core claim

We present a comprehensive tool-integrated reasoning recipe that prioritizes learnable teacher trajectories for supervised fine-tuning, controls the proportion of tool-use data to prevent forgetting of text-only capabilities, optimizes for pass@k and response length rather than loss, and follows with a stable reinforcement learning stage using verifiable rewards. When applied to thinking models at 4B and 30B scales, this recipe yields models that achieve state-of-the-art performance among open-source models on multiple benchmarks, including 96.7 percent on AIME 2025 for the 4B model and 99.2 percent for the 30B model.

What carries the argument

The TIR full-pipeline recipe, which combines selective SFT on tool-augmented trajectories with proportion control and a safeguarded RLVR stage to enable tool use while maintaining original reasoning.

If this is right

  • Thinking models can be extended to use tools effectively on problems suited for them.
  • Catastrophic forgetting of no-tool reasoning can be mitigated by balancing training data proportions.
  • Optimizing training for pass@k and length rather than loss leaves room for further RL improvements.
  • Stable RL with verifiable rewards provides effective final gains after proper SFT initialization.
  • Open-source models can reach performance levels like 99 percent on advanced math benchmarks such as AIME 2025.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method may allow smaller models to compete with larger ones by adding tool capabilities strategically.
  • Similar balancing techniques could apply to integrating other modalities or capabilities without interference.
  • Future work might test if the recipe generalizes to non-math domains like coding or science question answering.
  • Explicit safeguards against mode collapse in RL could become standard for tool-augmented training.

Load-bearing premise

That suitable teacher trajectories exist which are inherently learnable for tool solutions and that simply controlling their training proportion will reliably avoid forgetting without needing extra checks.

What would settle it

If models trained with the full recipe show either no improvement in tool-use tasks or significant drops in performance on pure text reasoning benchmarks compared to the base thinking models.

Figures

Figures reproduced from arXiv: 2605.06326 by Bowen Zhou, Ganqu Cui, Ning Ding, Qianjia Cheng, Shunkai Zhang, Yuchen Fan, Yu Cheng, Yuchen Zhang, Yun Luo, Yu Qiao, Yuxin Zuo, Zhilin Wang.

Figure 1
Figure 1. Figure 1: The same problem, two policies for invoking the tool. Grey boxes are text-only reasoning; violet-bordered In[k] cells are tool calls and sienna-bordered Out[k] cells are tool responses (Jupyter-style). Left: the pre-trained thinking model treats the Python sandbox as a nal-pass veri er  after a long text￾only Burnside derivation arrives at the impossible 2420/32=75.625, it makes one late call that hard-co… view at source ↗
Figure 2
Figure 2. Figure 2: TIR SFT dynamics of the 30B model. We perform SFT on Qwen3-30B-A3B-Thinking￾2507 using expert data generated by GPT-OSS-120B. The observed learning curve (measured on BeyondAIME) demonstrates a "form–substance–noise" progression. from 78.3% to 82.5%, while maintaining comparable BeyondAIME performance as presented in view at source ↗
Figure 3
Figure 3. Figure 3: The token proba￾bilities assigned by the infer￾ence and training engines to the same rollouts. During TIR SFT, the student model follows a form–substance–noise learning progression, which we characterize as stage 1-3. In the early stage, it quickly acquires the format of tool invocation, causing tool￾use frequency to rise sharply, which yet does not yield effective TIR. As shown in view at source ↗
Figure 4
Figure 4. Figure 4: The RL training dynamics of the 30B model, EP is short for Epoch. Ep 1 is the model in view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of different RL training strategies. An off-policy setting (four updates per view at source ↗
Figure 6
Figure 6. Figure 6: TIR token efficiency. Performance. As shown in view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of the roles code-executor plays in TRICE-30B trajectories on solved questions. 1) Code executor is not merely a calculator, but a cognitive tool. We annotate each solved trajectory with a primary com￾putational purpose with Gemini-3-Flash: empirical discovery, algorithmic search, computation offloading, or conjecture ver￾ification (definitions in Appendix D). As view at source ↗
Figure 8
Figure 8. Figure 8: Average prompt difficulty across SFT data configurations. Difficulty is mea￾sured by the avg@8 accuracy of GPT-OSS￾120B; higher values indicate easier prompts. For SFT, we need a prompt pool that is large enough to support selection rather than merely sampling. Nemotron-Math-v2 [9] provides such a source, con￾taining ∼347K mathematical problems with broad topic coverage. From this pool, we construct 65K tr… view at source ↗
Figure 9
Figure 9. Figure 9: TIR SFT dynamics of the 4B model. Under the same SFT data as the 30B model, Qwen3-4B-Thinking-2507 shows a similar “form–substance–noise” progression on BeyondAIME. Compared with the 30B model, it more readily produces long and truncated trajectories, making response length an important signal for checkpoint selection. B.4 More RL Training Dynamics 0 150 300 450 Training Steps 70 72 74 76 Acc. (%) (a) Accu… view at source ↗
Figure 10
Figure 10. Figure 10: RL dynamics of Qwen3-4B-Thinking-2507 after 12 SFT epochs on BeyondAIME. RL improves accuracy under noisy evaluations, while response length and tool-call counts rise, indicating more frequent tool use and a larger rollout budget demand. 0 80 160 240 320 Training Steps 76.5 78.0 79.5 81.0 Acc. (%) (a) Accuracy 0 80 160 240 320 Training Steps 24.0 25.5 27.0 28.5 Tokens (K) (b) Response length 0 80 160 240 … view at source ↗
Figure 11
Figure 11. Figure 11: RL dynamics of GLM-4.7-Flash after 4 SFT epochs on BeyondAIME. Starting from a model with native TIR ability, RL maintains high accuracy while steadily increasing response length and tool calls, suggesting stronger but more compute-intensive tool use. 19 view at source ↗
read the original abstract

Tool-integrated reasoning (TIR) offers a direct way to extend thinking models beyond the limits of text-only reasoning. Paradoxically, we observe that tool-enabled evaluation can degrade reasoning performance even when the strong thinking models make almost no actual tool calls. In this paper, we investigate how to inject natural tool-use behavior into a strong thinking model without sacrificing its no-tool reasoning ability, and present a comprehensive TIR recipe. We highlight that (i) the effectiveness of TIR supervised fine-tuning (SFT) hinges on the learnability of teacher trajectories, which should prioritize problems inherently suited for tool-augmented solutions; (ii) controlling the proportion of tool-use trajectories could mitigate the catastrophic forgetting of text-only reasoning capacity; (iii) optimizing for pass@k and response length instead of training loss could maximize TIR SFT gains while preserving headroom for reinforcement learning (RL) exploration; (iv) a stable RL with verifiable rewards (RLVR) stage, built upon suitable SFT initialization and explicit safeguards against mode collapse, provides a simple yet remarkably effective solution. When applied to Qwen3 thinking models at 4B and 30B scales, our recipe yields models that achieve state-of-the-art performance in a wide range of benchmarks among open-source models, such as 96.7% and 99.2% on AIME 2025 for 4B and 30B, respectively.

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.

Referee Report

2 major / 1 minor

Summary. The paper presents a full-pipeline recipe for tool-integrated reasoning (TIR) in thinking models. Key components include: (i) SFT on teacher trajectories selected for inherent learnability (prioritizing problems suited to tool-augmented solutions), (ii) controlling the proportion of tool-use trajectories during SFT to mitigate catastrophic forgetting of text-only reasoning, (iii) optimizing SFT for pass@k and response length rather than training loss to preserve RL headroom, and (iv) a stable RLVR stage with safeguards against mode collapse. Applied to Qwen3 thinking models at 4B and 30B scales, the recipe is claimed to yield open-source SOTA results, including 96.7% and 99.2% on AIME 2025 for the respective scales.

Significance. If the reported gains prove robust under controlled ablations, the work could meaningfully advance practical TIR training by addressing the observed paradox of tool-enabled evaluation degrading performance. The emphasis on trajectory learnability, proportion control, and pass@k optimization offers concrete, reproducible guidance for practitioners. The large-scale application to Qwen3 models and the comprehensive pipeline (SFT + RLVR) are strengths that distinguish it from narrower TIR studies.

major comments (2)
  1. [Abstract] Abstract: The headline SOTA claims (96.7% AIME 2025 for 4B, 99.2% for 30B) are presented without any reported base-model numbers, ablation results isolating the contribution of trajectory selection or proportion control, error bars, or data-split details. These omissions are load-bearing for the central empirical claim that the full TIR recipe (rather than the strong Qwen3 base) produces the gains.
  2. [SFT stage description] SFT stage description (points (i) and (ii)): The assertions that 'effectiveness hinges on the learnability of teacher trajectories' and that 'controlling the proportion of tool-use trajectories could mitigate catastrophic forgetting' are stated without concrete selection criteria for learnable trajectories, the specific proportion values employed, or quantitative forgetting metrics (e.g., no-tool benchmark deltas before/after SFT). This directly underpins the recipe's claimed reliability.
minor comments (1)
  1. [Abstract] The abstract refers to 'a wide range of benchmarks' but provides concrete numbers only for AIME 2025; adding one or two additional headline results with base-model comparisons would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments. We agree that the presentation of results and methodological details can be strengthened for clarity and reproducibility. Below, we provide point-by-point responses to the major comments and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline SOTA claims (96.7% AIME 2025 for 4B, 99.2% for 30B) are presented without any reported base-model numbers, ablation results isolating the contribution of trajectory selection or proportion control, error bars, or data-split details. These omissions are load-bearing for the central empirical claim that the full TIR recipe (rather than the strong Qwen3 base) produces the gains.

    Authors: We agree with the referee that the abstract would benefit from additional context to support the central claims. We will revise the abstract to include the base model performances on AIME 2025 and other benchmarks, as well as a brief mention of the ablation studies that isolate the effects of trajectory selection and proportion control. Furthermore, we will incorporate error bars from our experimental runs and provide data-split details in the methods section of the revised manuscript. These changes will clarify that the gains are attributable to the TIR recipe rather than solely the base model strength. revision: yes

  2. Referee: [SFT stage description] SFT stage description (points (i) and (ii)): The assertions that 'effectiveness hinges on the learnability of teacher trajectories' and that 'controlling the proportion of tool-use trajectories could mitigate catastrophic forgetting' are stated without concrete selection criteria for learnable trajectories, the specific proportion values employed, or quantitative forgetting metrics (e.g., no-tool benchmark deltas before/after SFT). This directly underpins the recipe's claimed reliability.

    Authors: We appreciate this feedback on the SFT stage description. The current manuscript presents these points at a high level based on our iterative development process. To enhance reproducibility and address the concern, we will expand the description with concrete selection criteria for learnable trajectories, the specific proportion values employed, and quantitative metrics on forgetting (e.g., no-tool benchmark deltas before/after SFT). These details will be added to the revised SFT stage section along with supporting tables or figures. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical training recipe with benchmark outcomes

full rationale

The paper describes an empirical pipeline for tool-integrated reasoning (TIR) on Qwen3 models, consisting of observations about tool-use degradation, SFT on learnable trajectories, proportion control, pass@k optimization, and RLVR. Performance numbers (e.g., 96.7%/99.2% on AIME 2025) are reported as direct training results on benchmarks. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text that would reduce any claim to its inputs by construction. The central claims remain falsifiable experimental outcomes rather than tautological re-statements.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on abstract only; no explicit free parameters, axioms, or invented entities are described. Standard assumptions of supervised fine-tuning and reinforcement learning with verifiable rewards are implicit but not enumerated.

pith-pipeline@v0.9.0 · 5590 in / 1219 out tokens · 51090 ms · 2026-05-08T10:23:58.675076+00:00 · methodology

discussion (0)

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