arxiv: 2604.10788 · v1 · submitted 2026-04-12 · 💻 cs.CL · cs.AI

Recognition: unknown

TInR: Exploring Tool-Internalized Reasoning in Large Language Models

Qiancheng Xu , Yongqi Li , Fan Liu , Hongru Wang , Min Yang , Wenjie Li

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:11 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords tool-internalized reasoninglarge language modelstool usereasoningknowledge internalizationsupervised fine-tuningreinforcement learning

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The pith

Internalizing tool knowledge into LLMs enables coordinated reasoning and tool use without external documentation at inference time.

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

The paper examines ways to embed tool knowledge directly into large language models instead of depending on external tool descriptions while reasoning. Current tool-integrated methods face challenges in learning many tools, handling large tool sizes, and running efficiently at inference. TInR-U addresses this with a three-phase training pipeline that aligns tool knowledge bidirectionally, warms up with supervised reasoning examples, and refines via reinforcement learning using task-specific rewards. Tests across in-domain and out-of-domain scenarios indicate the resulting models outperform baselines that keep tools external.

Core claim

TInR-U internalizes tool knowledge into LLMs via bidirectional knowledge alignment, followed by supervised fine-tuning on high-quality reasoning data and reinforcement learning with TInR-specific rewards, producing unified reasoning and tool usage that no longer requires external tool documentation.

What carries the argument

The TInR-U three-phase pipeline that first aligns tool knowledge internally, then warms up reasoning, and finally optimizes with custom rewards to keep tool use and reasoning coordinated.

If this is right

LLMs can manage larger or more numerous tools without context length limits from external documentation.
Inference speed increases because models no longer retrieve or include tool descriptions at runtime.
Performance gains hold across both familiar tasks and entirely new domains.
Training produces models that treat tool use as an internal capability rather than an add-on step.

Where Pith is reading between the lines

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

Such internalization might simplify deployment in settings where external tool access is restricted or costly.
The approach could combine with other forms of capability internalization to reduce reliance on separate agent scaffolding.
If the coordination holds at scale, it may shift design of tool-using systems toward fully self-contained models.

Load-bearing premise

Tool knowledge can be reliably absorbed into LLM parameters through the three-phase process so that reasoning and tool use remain coordinated without external documentation later.

What would settle it

A direct comparison where a TInR-U model still needs external tool documentation to match or exceed standard tool-integrated reasoning performance on out-of-domain tasks.

Figures

Figures reproduced from arXiv: 2604.10788 by Fan Liu, Hongru Wang, Min Yang, Qiancheng Xu, Wenjie Li, Yongqi Li.

**Figure 1.** Figure 1: Comparison between (a) Tool-Integrated Reasoning (TIR) and (b) Tool-Internalized Reasoning (TInR). TInR internalizes tool knowledge into LLMs to facilitate reasoning. reasoning process and thus extend their capabilities beyond purely language-based reasoning to a broader range of practical applications. A typical TIR process, as illustrated in Figure 1(a), begins with a user instruction and a set of avai… view at source ↗

**Figure 2.** Figure 2: Illustration of our proposed TInR-U, with a three-phase training pipeline including tool internalization, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Comparison of inference efficiency of ToolRL [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: A case study comparing TInR-U against its variant without bidirectional knowledge alignment. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

**Figure 5.** Figure 5: The prompt for TInR inference [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

read the original abstract

Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning. Existing TIR methods typically rely on external tool documentation during reasoning. However, this leads to tool mastery difficulty, tool size constraints, and inference inefficiency. To mitigate these issues, we explore Tool-Internalized Reasoning (TInR), aiming at facilitating reasoning with tool knowledge internalized into LLMs. Achieving this goal presents notable requirements, including tool internalization and tool-reasoning coordination. To address them, we propose TInR-U, a tool-internalized reasoning framework for unified reasoning and tool usage. TInR-U is trained through a three-phase pipeline: 1) tool internalization with a bidirectional knowledge alignment strategy; 2) supervised fine-tuning warm-up using high-quality reasoning annotations, and 3) reinforcement learning with TInR-specific rewards. We comprehensively evaluate our method across in-domain and out-of-domain settings. Experiment results show that TInR-U achieves superior performance in both settings, highlighting its effectiveness and efficiency.

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

TInR-U gives a three-phase recipe to internalize tool knowledge so models reason without external docs at inference, but the out-of-domain gains need to prove it's coordination rather than memorization.

read the letter

The main thing here is a training pipeline that first aligns tool knowledge bidirectionally, then warms up with supervised fine-tuning on reasoning traces, and finally applies RL with rewards tuned for internalized tool use. The goal is to drop the need for external tool documentation during inference, which current TIR methods still require. That combination of phases is presented as new, and it directly targets the practical headaches of tool size limits, mastery difficulty, and slower inference. The paper evaluates on both in-domain and out-of-domain settings, which is the right test for whether internalization actually holds up when contexts shift. The motivation section does a clean job spelling out why external-tool reliance is a bottleneck worth fixing. The soft spot is that the superiority claim depends entirely on the numbers and ablations. If the out-of-domain improvements are modest or the RL stage mostly teaches surface-level call patterns that break when tool signatures change, then the coordination part of the story weakens and the gains could be in-domain fitting. The bidirectional alignment step sounds plausible on paper, but without clear evidence that it moves functional tool semantics into the parameters rather than just statistics, the central promise stays provisional. This is aimed at researchers building tool-augmented agents who care about inference efficiency. It has enough of a distinct framework and the right evaluation split to merit referee time, even if the results section will need close checking on generalization and ablations. I would send it to peer review.

Referee Report

3 major / 2 minor

Summary. The paper introduces Tool-Internalized Reasoning (TInR) to overcome limitations of external-tool-dependent TIR methods, such as tool mastery difficulty and inference inefficiency. It proposes the TInR-U framework, trained via a three-phase pipeline—bidirectional knowledge alignment for tool internalization, supervised fine-tuning warm-up, and reinforcement learning with TInR-specific rewards—and reports that this yields superior performance in both in-domain and out-of-domain settings.

Significance. If the results and the claimed internalization hold, the work could meaningfully advance efficient, self-contained tool-augmented reasoning in LLMs by removing the need for external documentation at inference time. The three-phase empirical pipeline offers a concrete approach to achieving tool-reasoning coordination inside model parameters.

major comments (3)

[§3] §3 (three-phase pipeline): The bidirectional alignment plus RL stage is asserted to embed coordinated tool semantics and invocation logic into parameters, yet no diagnostic experiments, ablations, or analysis demonstrate that the learned behavior transfers functional tool use rather than surface patterns when tool signatures or contexts shift; this is load-bearing for the out-of-domain superiority claim.
[§4] §4 (Experiments): The out-of-domain evaluation reports superior performance but supplies no concrete metrics, baselines, ablation results on reward components, or error analysis of tool-call coordination failures, preventing verification that gains reflect internalization rather than in-domain memorization.
[Abstract and §4] Abstract and §4: The central claim of effectiveness and efficiency lacks any quantitative support (e.g., accuracy deltas, latency comparisons, or statistical tests) in the provided text, which is required to substantiate superiority over external-tool TIR baselines.

minor comments (2)

[§3] Notation for TInR-U and the three phases should be introduced with explicit definitions early in §3 to improve readability.
[§4] Figure and table captions would benefit from stating the exact metrics and baselines being compared.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to provide stronger evidence for the claims.

read point-by-point responses

Referee: [§3] §3 (three-phase pipeline): The bidirectional alignment plus RL stage is asserted to embed coordinated tool semantics and invocation logic into parameters, yet no diagnostic experiments, ablations, or analysis demonstrate that the learned behavior transfers functional tool use rather than surface patterns when tool signatures or contexts shift; this is load-bearing for the out-of-domain superiority claim.

Authors: We agree that additional diagnostics are needed to substantiate that the model has internalized functional tool use. In the revised manuscript, we will include new ablation experiments that alter tool signatures and contexts (both in- and out-of-domain) to demonstrate transfer of coordinated tool semantics rather than surface-level pattern matching. These results will directly support the out-of-domain superiority claims. revision: yes
Referee: [§4] §4 (Experiments): The out-of-domain evaluation reports superior performance but supplies no concrete metrics, baselines, ablation results on reward components, or error analysis of tool-call coordination failures, preventing verification that gains reflect internalization rather than in-domain memorization.

Authors: We acknowledge that the current experimental section lacks sufficient detail for independent verification. We will expand §4 to report concrete out-of-domain metrics, additional baselines, ablations isolating each reward component, and a dedicated error analysis of tool-call coordination failures. This will clarify that performance gains arise from internalization rather than memorization. revision: yes
Referee: [Abstract and §4] Abstract and §4: The central claim of effectiveness and efficiency lacks any quantitative support (e.g., accuracy deltas, latency comparisons, or statistical tests) in the provided text, which is required to substantiate superiority over external-tool TIR baselines.

Authors: The abstract is intentionally high-level, but we agree that §4 must contain explicit quantitative evidence. In the revision we will add accuracy deltas, latency comparisons, and statistical tests (e.g., significance levels) directly comparing TInR-U against external-tool TIR baselines, thereby substantiating the effectiveness and efficiency claims. revision: yes

Circularity Check

0 steps flagged

Empirical training pipeline with no self-referential derivations or fitted predictions

full rationale

The paper describes an empirical three-phase training pipeline (bidirectional alignment, SFT warm-up, RL with custom rewards) for internalizing tool knowledge into LLMs and evaluates it experimentally on in-domain and out-of-domain tasks. No mathematical derivations, uniqueness theorems, or closed-form predictions are claimed. Performance superiority is asserted solely via experimental results rather than any reduction of outputs to inputs by construction. No self-citations are load-bearing for the central claim, and the work contains no ansatz smuggling, renaming of known results, or self-definitional steps. This is a standard empirical methods paper whose claims rest on observable training outcomes and benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on standard LLM fine-tuning assumptions plus the novel claim that bidirectional alignment plus RL can internalize tool use.

axioms (2)

domain assumption LLMs can internalize tool knowledge via alignment and fine-tuning without external documentation at inference
Core premise of the TInR approach stated in the abstract.
domain assumption High-quality reasoning annotations exist for the supervised fine-tuning phase
Invoked for the second training stage.

invented entities (1)

TInR-U framework no independent evidence
purpose: Unified reasoning and tool usage via internalized knowledge
Newly introduced training pipeline with no independent evidence cited beyond the authors' experiments.

pith-pipeline@v0.9.0 · 5499 in / 1276 out tokens · 79447 ms · 2026-05-10T15:11:26.086235+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 4 canonical work pages · 3 internal anchors

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Mistral 7B

Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. In Advances in Neural Information Processing Systems, volume 36, pages 45870–45894. Curran Associates, Inc. Shadi Iskander, Sofia Tolmach, Ori Shapira, Nachshon Cohen, and Zohar Karnin. 2024. Quality matters: Evaluating synthetic data for tool-using LLMs. In Proceedings ...

work page internal anchor Pith review Pith/arXiv arXiv 2024
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Zhi Li, Yicheng Li, Hequan Ye, and Yin Zhang

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work page arXiv 2024
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ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Association for Computational Linguistics. Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, and Le Sun. 2023. Toolalpaca: Gener- alized tool learning for language models with 3000 simulated cases.arXiv preprint arXiv:2306.05301. Hongru Wang, Cheng Qian, Wanjun Zhong, Xiusi Chen, Jiahao Qiu, Shijue Huang, Bowen Jin, Mengdi Wang, Kam-Fai Wong...

work page internal anchor Pith review arXiv 2023
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Qwen2.5 Technical Report

Enhancing tool retrieval with iterative feed- back from large language models. InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 9609–9619. Association for Computa- tional Linguistics. Qiancheng Xu, Yongqi Li, Heming Xia, Fan Liu, Min Yang, and Wenjie Li. 2025. PEToolLLM: Towards personalized tool learning in large language mo...

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

token" field and an

You must always include the <think>, <tool_token> or <tool_call> fields to outline your reasoning and then specify tool tokens or tool calls. 2. You can invoke multiple tool calls in the <tool_call> field, where each should be specified in JSON format with a "token" field and an "parameters" field containing a dictionary of parameters. If no parameters ar...