MidTool: Mid-training Data Synthesis for Agentic Tool Use

Fengqing Jiang; Yite Wang; Boyi Liu; Zhaoyang Wang; Canwen Xu; Zhewei Yao; Radha Poovendran; Yuxiong He

cs.AIAug 20, 2026

MidTool: Mid-training Data Synthesis for Agentic Tool Use

Authors: Fengqing Jiang, Yite Wang, Boyi Liu, Zhaoyang Wang, Canwen Xu, Zhewei Yao, Radha Poovendran, Yuxiong He

Organizations: University of Washington · Snowflake · University of North Carolina at Chapel Hill

Abstract

Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models. Recent work has shown that targeted mid-training can strengthen reasoning-intensive abilities such as math and science, and can also improve agentic capabilities in software-engineering settings. In this work, we study the parallel but less explored agentic capability: general tool use. We present MidTool, an open corpus construction pipeline for agentic tool-use mid-training that combines large-scale web, PDF, and code data with synthesized supervision from real-world tool APIs, MCP skills, and document-grounded workflows. MidTool is designed to teach models how to recognize tool affordances, ground arguments from context, compose tool call workflow, and recover from incomplete information. We mid-train Qwen3-4B-Base and Qwen3-8B-Base on MidTool-Mix, and then apply follow-up post-training with both supervised fine-tuning and reinforcement learning. Compared with baselines, MidTool-Mix consistently improves downstream performance under both SFT and RL on BFCL, tau2-Bench, and MCP Universe. These results suggest that general tool use, like other important LLM capabilities, benefits from dedicated mid-training rather than being left entirely to post-training.

Figures & tables

Figure 1: Left: MidTool-Mix is a 20.3B-token mid-training corpus built from web, PDF, code, and tool sources with synthesized agentic trajectories. Middle: Training pipeline from base model. Right: On MCP-Universe, our mid-trained 4B and 8B models outperform Qwen3 official models, showing that dedicated mid-training yields stronger agentic capability than scaling alone.

Panel of Table 1 — Table 1: Comparison of representative work on pre-training (PT) and mid-training (MT) corpora. Tool Div. = tool diversity and Agent Traj. = (include) agentic trajectories.

Figure 2: Overview of MidTool pipeline. Stage 1 collects four complementary source families, including web pages, PDFs, code repositories, and structured tool artifacts. Stage 2 applies source-specific preprocessing, quality control, and deduplication. Stage 3 converts the refined corpus into executable supervision through two branches: context-grounded trajectory augmentation from web/PDF/code documents, and native agentic trajectory synthesis from real APIs and MCP skills.

Model Setting	Single Turn		Multi-Turn					Hallu.	Overall
Model Setting	Non-live	Live	Base	MF	MP	LC	Avg.	Hallu.	Overall
Qwen3-4B	39.58%	24.35%	10.50%	9.00%	6.00%	10.00%	8.88%	9.05%	24.27%
Qwen3-4B-Base + SFT	59.94%	43.75%	21.50%	14.00%	10.00%	16.50%	15.50%	60.46%	39.73%
Qwen3-4B-Base + SFT + RL	59.65%	39.90%	27.00%	18.00%	13.00%	18.00%	19.00%	55.19%	39.51%
Qwen3-4B-Base + MidTool-Mix + SFT	66.38%	57.74%	36.00%	23.00%	19.50%	28.00%	26.63%	56.95%	50.25%
Qwen3-4B-Base + MidTool-Mix + SFT + RL	76.44%	58.48%	34.50%	24.50%	20.50%	31.00%	27.63%	60.10%	54.18%

Table 3: BFCLv3 results for Qwen3-4B and Qwen3-8B base models. For each scale, we compare SFT and SFT+RL with and without prior mid-training on MidTool-Mix ; released Qwen3 models are included as references. We report single-turn, multi-turn (MF = Missing Function, MP = Missing Parameters, LC = Long Context), hallucination (Hallu.), and overall performance. All values are percentages and higher is better.

Model Setting	Airline		Retail		Telecom		Overall
Model Setting	Pass@1	Pass@4	Pass@1	Pass@4	Pass@1	Pass@4	Pass@1	Pass@4
Qwen3-4B	21.00%	32.00%	16.23%	35.09%	3.51%	5.26%	11.87%	22.30%
Qwen3-4B-Base + SFT	17.50%	34.00%	9.43%	24.56%	3.73%	10.53%	8.54%	20.50%
Qwen3-4B-Base + SFT + RL	31.00%	52.00%	18.20%	39.47%	0.00%	0.00%	13.04%	25.54%
Qwen3-4B-Base + MidTool-Mix + SFT	17.00%	38.00%	20.83%	47.37%	1.54%	4.39%	12.23%	28.06%
Qwen3-4B-Base + MidTool-Mix + SFT + RL	20.00%	40.00%	33.55%	57.89%	6.36%	18.42%	19.96%	38.49%

Table 4:

\tau^{2}

-Bench results for Qwen3-4B and Qwen3-8B base models. For each scale, we compare SFT and SFT+RL with and without prior mid-training on MidTool-Mix ; released Qwen3 models are included as references. We report Pass@1 and Pass@4 for airline, retail, telecom, and overall; higher score is stronger interactive capacity.

Model Setting	Browser Auto.		Financial		Location		Multi-server		Web Search		Overall
Model Setting	Score	Pass	Score	Pass	Score	Pass	Score	Pass	Score	Pass	Score	Pass
Qwen3-4B	19.42	5.88%	9.17	7.50%	43.51	2.86%	16.17	0.00%	0.00	0.00%	16.05	3.35%
Qwen3-4B-Base + SFT	15.44	2.94%	5.00	5.00%	38.98	0.00%	13.67	0.00%	0.00	0.00%	13.20	1.68%
Qwen3-4B-Base + SFT + RL	17.65	5.88%	6.67	5.00%	39.69	0.00%	17.00	0.00%	0.00	0.00%	14.50	2.23%
Qwen3-4B-Base + MidTool-Mix + SFT	21.81	8.82%	14.27	12.50%	46.81	2.86%	19.50	0.00%	0.00	0.00%	18.66	5.03%
Qwen3-4B-Base + MidTool-Mix + SFT + RL	19.73	8.82%	38.33	37.50%	49.50	0.00%	16.17	0.00%	0.00	0.00%	23.80	10.06%

Table 5: MCP-Universe results for Qwen3-4B and Qwen3-8B base models. For each scale, we compare SFT and SFT+RL with and without prior mid-training on MidTool-Mix ; released Qwen3 models are included as references. We report score and pass rate; higher is better.

Mid-training Data	BFCLv3				$\tau^{2}$ -Bench		MCP-Universe
Mid-training Data	Non-live	Live	Multi-turn	Overall	Pass@1	Pass@4	Score	Pass
No Mid-training	59.94% +0.0	43.75% +0.0	15.50% +0.0	39.73% +0.0	8.54% +0.0	20.50% +0.0	13.20 +0.0	1.68% +0.0
Dolmino-20BT	61.44% +1.5	51.74% +8.0	16.13% +0.6	43.10% +3.4	7.37% -1.2	21.22% +0.7	5.41 -7.8	0.00% -1.68
MidTool
Processed data w/o traj.	60.40% +0.5	52.60% +8.9	13.90% -1.6	42.30% +2.6	7.30% -1.2	21.90% +1.4	12.20 -1.0	3.03% +1.4
+ native agentic traj.	68.21% +8.3	55.81% +12.1	18.75% +3.3	47.59% +7.9	4.23% -4.3	12.95% -7.6	6.80 -6.4	1.12% -0.6

Table 6: Ablation across BFCLv3,

\tau^{2}

-Bench, and MCP-Universe under the Qwen3-4B-Base + SFT setting, where the downstream post-training recipe is fixed and only the mid-training corpus is changed. Rows under MidTool are additive over the filtered raw sources: Processed data w/o traj. mid-trains on the processed raw data only, with no synthesized trajectories; the next two rows each add a single synthesis branch on top of it, and are therefore alternatives rather than successive steps; MidTool-Mix is the complete mixture combining all. Dolmino-20BT is a matched-budget generic mid-training baseline.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Slice	source-only	$+$ QA	$+$ QA $+$ traj.
web (6.86M)	36.4%	52.2%	11.3%
pdf (1.34M)	30.8%	13.4%	55.7%
code (2.60M)	68.9%	4.2%	26.8%

Appendix

Table 7: Augmentation type per source slice, as a fraction of samples within that slice. Sample counts match the Samples column of Table 2 .

Metric	Stat	web	pdf	code	native
Turns	mean	2.8	3.0	3.1	4.0
	median	3	3	3	4
	max	15	17	8	69
Steps	mean	5.3	7.1	6.0	3.8
	median	5	6	5	4
	max	54	77	58	21

Appendix

Table 8: Per-document trajectory statistics among samples containing agentic trajectories. A turn is a user message, a step is an assistant message, and a tool call is a single tool invocation.

Category	Calls	Share	Examples
Code execution / Shell / DevOps	862K	10.9%	run_command , kubectl_get
List / Get (generic)	811K	10.2%	list_tools , get_*
File / Filesystem	380K	4.8%	read_file , list_dir
Set / Update / Modify	354K	4.5%	set_* , configure
Add / Create	348K	4.4%	create_* , register_*
Search / Lookup	335K	4.2%	search , lookup

Appendix

Table 9: Functional categories of tool calls in MidTool-Mix , obtained by keyword mapping over tool names.

Hyperparameter	Value
Epochs	1
Max Sequence length	8192
Optimizer	AdamW
Learning rate	$3\times 10^{-5}$
Betas	$(0.9,0.999)$
Weight decay	0.01

Appendix

Table 10: Mid-training hyperparameters for Qwen3-4B-Base and Qwen3-8B-Base .

Hyperparameter	Value
Max Sequence length	32768
Optimizer	AdamW
Learning rate	$2\times 10^{-5}$
Betas	$(0.9,0.95)$
Weight decay	0.01
LR schedule	cosine, warmup ratio 0.001

Appendix

Table 11: Supervised fine-tuning hyperparameters.

Hyperparameter	Value
RL algorithm	GRPO
Total training steps	64
Learning rate	4B: $1\times 10^{-6}$ ; 8B: $5\times 10^{-7}$
Data Batch size	32
PPO Mini-batch size	32
Rollouts per prompt	16

Appendix

Table 12: Reinforcement-learning hyperparameters.

Figure 4: SFT loss trajectories on the same downstream tool-use corpus for three Qwen3-4B initializations. MidTool-Mix starts from a lower loss, converges faster, and maintains the best loss throughout training compared with both the raw base model and the Dolmino mid-trained baseline.

Panel of Figure 5 — Figure 5: Average RL reward during training for 4B (left) and 8B (right) models. In both scales, MidTool-Mix starts from a stronger point and improves more quickly in the early stage of RL, while the non-mid-trained baseline gradually catches up later within the same training environment.

Model	Tool Succ.	Overall	STEM	Med	Fin	Sprt	Gen
Llama4-Maverick ∗	-	0.1545	0.1875	0.1581	0.1182	0.1562	0.1524
Gemma3-4B-pt + SFT	0.5863	0.0567	0.0655	0.0600	0.0573	0.0564	0.0448
Gemma3-4B-pt + MidTool-Mix + SFT	0.7231	0.0661	0.0733	0.0765	0.0640	0.0563	0.0606

Appendix

Table 13: Exploratory results on the single-turn subset of VisualToolBench. We fine-tune the base model on a FineVision subset and then on the same text-only tool-use SFT dataset as in our main setting. We report tool success rate and average rubric score; higher is better. Llama4-Maverick score is reported from the original benchmark paper for reference.

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SPT: Skills as Pre-Training Data for Agentic Language Models

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Open paper AskPDF arXiv

Agentic (tool-using) language models are mainly trained on tool-call traces and agent trajectories during post-training. These data provide direct behavioral supervision, but producing them requires task environments, execution, and verification, making broad tool and task coverage expensive. Publicly available skills offer another source of training data: they encode reusable tool semantics and workflows but are typically used only as inference-time context. We introduce Skill Pre-Training (SPT), a mid-training method that applies causal language modeling to SkillCorpus, a collection of public multi-file skill packages, optionally mixed with general data. To preserve relations among files within each package, we also introduce Reference Insert, a reference-aware assembly strategy that places supporting files near their mentions in the primary instruction. Experiments across multiple model scales and post-training recipes show that SPT consistently improves agentic performance over mid-training on general or trajectory data, while largely preserving general performance. Data mixture experiments show additional benefits from combining skill data with general annealing corpora. These results indicate that skill packages are a valuable data source for pre-training agentic language models.

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