arxiv: 2604.13787 · v1 · submitted 2026-04-15 · 💻 cs.CL

Recognition: unknown

ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution

Shouzheng Huang , Meishan Zhang , Baotian Hu , Min Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:01 UTC · model grok-4.3

classification 💻 cs.CL

keywords open-world tool useagentic frameworkproactive retrievalgrounded executiontool learninglanguage model agentsmulti-objective optimization

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The pith

ToolOmni enables language models to handle open-world tool use by interleaving proactive retrieval with grounded execution in a reasoning loop.

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

The paper seeks to show that language models can overcome poor alignment with tool semantics and failure to generalize to new tools by shifting from static retrieval or memorization to an active agentic process. In this process the model reasons step by step, decides when to search a large evolving tool set, retrieves candidates, and then executes them while staying grounded in the current task. A cold-start supervised phase builds basic multi-turn interaction skills, after which a decoupled multi-objective optimization algorithm improves retrieval accuracy and execution success together in live environments. If the approach holds, models could maintain high performance as tool collections grow or change without requiring constant retraining or fixed embeddings.

Core claim

ToolOmni is a unified agentic framework that equips LLMs for open-world tool use through proactive retrieval and grounded execution inside a reasoning loop. Foundational agentic behavior is instilled via supervised fine-tuning on a cold-start multi-turn interaction dataset. Open-world tool learning then proceeds with a Decoupled Multi-Objective GRPO algorithm that jointly optimizes retrieval accuracy and execution efficacy in online settings, producing state-of-the-art results with a reported 10.8 percent gain in end-to-end execution success rate plus strong robustness to unseen tools.

What carries the argument

The agentic reasoning loop that interleaves proactive tool retrieval decisions with grounded execution steps, refined by a decoupled multi-objective optimization algorithm.

If this is right

Tool retrieval accuracy rises because the model learns to issue targeted searches rather than relying on fixed embeddings.
End-to-end task success increases by more than 10 percent when retrieval and execution are optimized together.
Performance on previously unseen tools improves without additional memorization or retraining.
The system remains effective when the underlying tool repository grows or changes over time.

Where Pith is reading between the lines

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

The same interleaving of search and execution could be tested on other dynamic external knowledge sources such as databases or APIs.
Separating the two objectives during optimization may reduce the need for post-training adjustments in other agentic systems.
Continuous online refinement of the kind described could support agents that adapt to tools introduced after deployment.

Load-bearing premise

The assumption that the decoupled optimization can improve both retrieval accuracy and execution success at the same time without creating hidden performance trade-offs that the chosen benchmarks do not reveal.

What would settle it

A controlled test that adds a large number of new tools after training and measures whether end-to-end execution success falls below the levels achieved by the static-retrieval baselines.

Figures

Figures reproduced from arXiv: 2604.13787 by Baotian Hu, Meishan Zhang, Min Zhang, Shouzheng Huang.

**Figure 1.** Figure 1: Motivation for ToolOmni in Open-World Scenarios: Embedding retrieval methods struggle with Massive tools, often resulting in low retrieval accuracy due to shallow matching; Parameter memory methods fail to adapt to Evolving tools, suffering from poor generalization to unseen tools. ToolOmni overcomes these limitations via a unified agentic framework that couples Proactive Retrieval with Grounded Execution… view at source ↗

**Figure 2.** Figure 2: Overview of the ToolOmni framework. The pipeline operates in two decoupled phases: Proactive Retrieval: The agent iteratively interacts with the retrieval server to curate a candidate tool set. Grounded Execution: With retrieval results, the agent performs reasoning and tool invocation to generate the final answer. dynamics, thereby stabilizing the policy gradient optimization. 3.3 Open-world Tool Learning… view at source ↗

**Figure 3.** Figure 3: Ablation on retrieval strategy. Iterative Retrieval boosts NDCG@5 over the Oneshot baseline. tively identifies functionally similar alternative tools within the adversarial set, showcasing a flexible reasoning capability that leverages noise to mitigate single-tool failures, transcending rigid groundtruth matching(Cuconasu et al., 2024). 4.5 Ablation Study(RQ4) To validate the contribution of our core… view at source ↗

**Figure 5.** Figure 5: Ablation study of RL components on the Tool [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Sensitivity analysis of the format reward [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: Qualitative comparison (Case Study). Case 1 illustrates how pipeline baselines fail due to retrieval noise (selecting basic Search instead of Advanced Search) and missing tools (Streaming), leading to hallucinations. In contrast, ToolOmni’s proactive iterative retrieval precisely locates the golden toolset, enabling correct execution and grounded response generation. Note: The iterative process shown is co… view at source ↗

**Figure 8.** Figure 8: Qualitative comparison (Case Study). Case 2 highlights the robustness of ToolOmni against execution failures. While ToolLlama gets trapped in a repetitive error loop due to rigid parameter usage, ToolOmni demonstrates adaptive planning: after tool failures, it dynamically pivots its strategy—switching from general search to specific verification—to successfully fulfill the user request. Note: The process i… view at source ↗

**Figure 9.** Figure 9: System prompts used for Retrieval (Left) and Execution (Right) phases. The retrieval prompt guides the agent to proactively search and select tools, while the execution prompt instructs it to perform grounded reasoning and tool invocation [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗

read the original abstract

Large Language Models (LLMs) enhance their problem-solving capability by utilizing external tools. However, in open-world scenarios with massive and evolving tool repositories, existing methods relying on static embedding retrieval or parameter memorization of tools struggle to align user intent with tool semantics or generalize to unseen tools, respectively, leading to suboptimal accuracy of open-world tool retrieval and execution. To address these, we present ToolOmni, a unified agentic framework that enables LLMs for open-world tool use by proactive retrieval and grounded execution within a reasoning loop. First, we construct a cold-start multi-turn interaction dataset to instill foundational agentic capabilities via Supervised Fine-Tuning (SFT). Then, we introduce open-world tool learning based on a Decoupled Multi-Objective GRPO algorithm, which simultaneously optimizes LLMs for both tool retrieval accuracy and execution efficacy in online environments. Extensive experiments demonstrate that ToolOmni achieves state-of-the-art performance both in retrieval and execution, surpassing strong baselines by a significant margin of +10.8% in end-to-end execution success rate, while exhibiting exceptional robustness and generalization capabilities.

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

ToolOmni's two-stage SFT-plus-decoupled-GRPO pipeline targets proactive retrieval and execution in open tool sets, but the +10.8% claim needs clear reward definitions and ablations to rule out trade-offs or tuning artifacts.

read the letter

ToolOmni's core idea is a cold-start SFT phase on multi-turn interaction data, followed by a Decoupled Multi-Objective GRPO stage that trains the model to retrieve tools proactively inside its reasoning loop while also improving execution success. This directly tackles the limits of static embedding retrieval and parameter memorization when tool repositories are large and evolving. The paper does a solid job spelling out why those older approaches fall short for generalization to unseen tools and then offers a concrete pipeline that keeps retrieval active rather than one-shot. If the full experiments hold up with proper baselines and tests on dynamic tool sets, the reported gains in both retrieval and end-to-end execution would be useful for agent work. The main soft spot is the GRPO component itself. The abstract claims simultaneous optimization of retrieval accuracy and execution efficacy, yet supplies no equations for the separate reward signals or loss weighting. Without those details or ablations showing the two objectives do not interfere, the performance margin could depend on implementation choices or selective tuning after initial runs. The stress-test concern about hidden trade-offs is reasonable until the paper shows stability across tool repositories. This work is aimed at researchers building LLM agents for practical tool use in changing environments. The problem is real and the proposed training split has enough novelty to justify a serious referee who can check the reward formulations, ablations, and reproducibility. I would send it to peer review.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces ToolOmni, a unified agentic framework for open-world tool use by LLMs. It constructs a cold-start multi-turn interaction dataset for supervised fine-tuning (SFT) to instill foundational agentic capabilities, then applies a Decoupled Multi-Objective GRPO algorithm to jointly optimize tool retrieval accuracy and execution efficacy in online environments. Experiments claim state-of-the-art performance, with a +10.8% gain in end-to-end execution success rate over strong baselines, plus robustness and generalization to unseen tools.

Significance. If the performance claims are substantiated, the work could meaningfully advance tool-augmented LLMs by tackling dynamic, evolving tool repositories where static retrieval and memorization fail. The combination of proactive retrieval inside a reasoning loop with grounded execution is a coherent architectural choice. Credit is due for the explicit two-stage pipeline (SFT followed by online GRPO) and for attempting to demonstrate generalization beyond the training tool set.

major comments (3)

[§4.2] §4.2 (Decoupled Multi-Objective GRPO): The algorithm is described as simultaneously optimizing retrieval accuracy and execution efficacy, yet no explicit reward functions, loss weighting scheme, or decoupling constraints are provided. This is load-bearing for the central +10.8% end-to-end claim, as the skeptic concern about hidden trade-offs or post-hoc tuning cannot be evaluated without these definitions.
[Table 2] Table 2 (main results): The reported +10.8% end-to-end execution success margin is presented without standard deviations across runs, number of evaluation seeds, or statistical significance tests. This undermines confidence that the margin reflects a stable improvement rather than variance or selective reporting.
[§5.2] §5.2 (ablations): No ablation isolating the effect of the multi-objective decoupling (e.g., single-objective GRPO variants or varying reward weights) is reported. Without such controls, it is impossible to confirm that the joint optimization avoids negative interference between retrieval and execution objectives in online settings.

minor comments (2)

The abstract and §1 claim 'exceptional robustness and generalization capabilities,' but the main text should include quantitative breakdowns (e.g., success rates on held-out tool categories or repository sizes) rather than qualitative statements.
[§4.2] Notation for the GRPO objectives is introduced without a clear mapping to the online environment feedback signals used during training.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and describe the revisions we will incorporate to strengthen the manuscript.

read point-by-point responses

Referee: [§4.2] §4.2 (Decoupled Multi-Objective GRPO): The algorithm is described as simultaneously optimizing retrieval accuracy and execution efficacy, yet no explicit reward functions, loss weighting scheme, or decoupling constraints are provided. This is load-bearing for the central +10.8% end-to-end claim, as the skeptic concern about hidden trade-offs or post-hoc tuning cannot be evaluated without these definitions.

Authors: We agree that the manuscript does not supply the explicit mathematical definitions of the reward functions, weighting scheme, or decoupling constraints in §4.2. The Decoupled Multi-Objective GRPO separates the objectives by computing an independent retrieval reward (based on tool selection accuracy) and execution reward (based on task success), then alternates policy-gradient updates between the two to reduce interference. We will revise §4.2 to include the precise reward formulations, the loss-weighting scheme, and the decoupling constraints. revision: yes
Referee: [Table 2] Table 2 (main results): The reported +10.8% end-to-end execution success margin is presented without standard deviations across runs, number of evaluation seeds, or statistical significance tests. This undermines confidence that the margin reflects a stable improvement rather than variance or selective reporting.

Authors: We acknowledge that the current Table 2 lacks measures of variability and statistical testing. We will update the table to report standard deviations from our existing multi-run experiments, state the number of evaluation seeds, and add the results of statistical significance tests. revision: yes
Referee: [§5.2] §5.2 (ablations): No ablation isolating the effect of the multi-objective decoupling (e.g., single-objective GRPO variants or varying reward weights) is reported. Without such controls, it is impossible to confirm that the joint optimization avoids negative interference between retrieval and execution objectives in online settings.

Authors: We agree that an explicit ablation isolating the decoupling mechanism is missing from §5.2. While the main results compare against single-objective baselines, we will add a dedicated ablation subsection that varies reward weights and contrasts decoupled versus joint optimization to demonstrate reduced negative interference. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper describes a two-stage process: SFT on a constructed cold-start multi-turn dataset to instill agentic capabilities, followed by application of the Decoupled Multi-Objective GRPO algorithm to jointly optimize retrieval accuracy and execution efficacy in online settings. The +10.8% end-to-end execution success rate and SOTA claims are presented as outcomes of extensive experiments comparing against baselines, not as quantities derived by construction from the training objectives or inputs. No equations, reward formulations, or self-citations are shown in the abstract that would reduce the performance metric to a fitted parameter or tautological redefinition. The framework's central claims rest on empirical results from held-out evaluation rather than self-referential definitions or load-bearing internal citations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the unproven premise that a cold-start multi-turn dataset can instill general agentic capabilities and that the GRPO objective can be decoupled without introducing new fitting parameters that are tuned to the reported metrics.

axioms (1)

domain assumption Supervised fine-tuning on the constructed cold-start dataset instills foundational agentic capabilities
Invoked as the first training stage before GRPO.

pith-pipeline@v0.9.0 · 5500 in / 1299 out tokens · 66727 ms · 2026-05-10T14:01:00.572222+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references · 5 canonical work pages · 2 internal anchors

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InEMNLP (Findings), pages 4705–4726

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work page arXiv 2024
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Qiantong Xu, Fenglu Hong, Bo Li, Changran Hu, Zhengyu Chen, and Jian Zhang

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work page internal anchor Pith review Pith/arXiv arXiv 2022
[6]

golden tools

Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information pro- cessing systems, 36:46595–46623. Yuanhang Zheng, Peng Li, Wei Liu, Yang Liu, Jian Luan, and Bin Wang. 2024. Toolrerank: Adap- tive and hierarchy-aware reranking for tool retrieval. arXiv preprint arXiv:2403.06551. A Data Curation A.1 ToolBench ToolBench (Qin et al.,...

work page arXiv 2024
[7]

B.3 Implementation Details

again to assess the performance of unified generative frameworks in end-to-end scenarios. B.3 Implementation Details. We initialize ToolOmni upon the Qwen3-4B- Instruct (Yang et al., 2025). Regarding the re- ward configuration, we set the format weight to 0.2 and the performance weight to 0.8 for both phases (i.e., α1 = 0.2, α 2 = 0.8 for retrieval; β1 = ...

2025
[8]

category

Initial Tool Call (Failure) <tool_call> {"category": "Advertising", "tool_name": "Reqres", "api_name": "Users", "tool_input": {}}</tool_call>
[9]

error":

Environment Feedback (Simulated Error) <information> {"error": "Missing input parame- ters.", "response": ""}</information>
[10]

User by id

Model Reasoning & Adaptive Adjustment <reasoning> The initial call to retrieve user lists failed due to missing parameters. I will now switch to the "User by id" tool, providing the specific ID ’1’ to recover from this error.</reasoning>
[11]

category

Refined Tool Call (Success) <tool_call> {"category": "Advertising", "tool_name": "Reqres", "api_name": "User by id", "tool_input": {"id": "1"}}</tool_call>
[12]

error":

Final Environment Feedback <information> {"error": "", "response": "{"user_id": 1, "name": "John Doe", "email": "john.doe@example.com"}"}</information> This execution flow confirms that our hybrid environment not only provides realistic feedback but also effectively evaluates the model’s ability to detect, reason about, and recover from execution failures...

2000
[14]

The content inside <final_tools> and </final_tools> must be a list of useful api ids selected directly from earlier <information> blocks
[15]

Do not invent new apis

Remove duplicates if an api appears multiple times. Do not invent new apis. Question: {question} Execution Prompt You are an AutoGPT for tool calling, capable of utilizing tools and functions to complete the given question. Given the user question, your task is to understand the user’s intents and call the most appropriate tool(s) in a logical sequence to...
[16]

Do not provide any explanations outside the tags
[17]

The content inside <tool_call> and </tool_call> must include the selected tool’s category, tool_name, api_name, and the required input arguments
[18]

The retrieval prompt guides the agent to proactively search and select tools, while the execution prompt instructs it to perform grounded reasoning and tool invocation

You can only use the tools in available tools: {available_tools} Question: {question} Figure 9: System prompts used forRetrieval(Left) andExecution(Right) phases. The retrieval prompt guides the agent to proactively search and select tools, while the execution prompt instructs it to perform grounded reasoning and tool invocation