Deep research agents are increasingly deployed to retrieve precise information from large corpora, yet multi-hop, long-horizon tasks remain challenging. Synthetic data generation has emerged as a scalable solution, with existing pipelines typically expanding seed tasks to increase their apparent depth and breadth. However, this expansion does not reliably translate into necessary reasoning: newly introduced evidence may be only weakly coupled to the answer, creating a mismatch between intended and empirical difficulty. To address this problem, we introduce GRADS, a graph-guided agentic data synthesis framework with explicit difficulty control. GRADS runs a closed loop of seed selection, evidence expansion, question--answer construction, and verification while maintaining a graph of the evidence and inference dependencies required to derive each answer. At each step, it conditions generation on a relevant subgraph, preserving indispensable inference without accumulating irrelevant context. Experiments across security, software-engineering, and scientific-document domains show that GRADS consistently generates verified tasks with empirically separated difficulty distributions, as measured by independent solvers. Moreover, under matched training budgets, post-training on this data delivers consistent gains at every level: SFT raises the solution-termination rate to over 95% while cutting tool calls and turns by up to 75%, RL trims tool use by up to a further 19.5%, and on our held-out, domain-specific benchmarks, the resulting 4B model matches Sonnet~4 on software engineering and surpasses it on the scientific and security domains.
Figures & tables
Figure 1: Graph expansion. Exploration adds provenance-linked claim nodes and necessary-dependency edges across rounds; reasoning depth accumulates until the requested band is reached, at which point a subgraph of the target depth is selected and verbalized into one multi-hop question.
Figure 2: Data synthesis agent flow. A stateless Brain drives the loop from the selected context. (1) Beginning: the Sampler draws a seed and the Analyst sets the generation target. (2) Main Workflow: each round, Explorer , Analyst , Respondent , Questioner , and Verifier build and check one QA item (green = information, red = feedback). (3) Context Management: the Graph Builder , Graph Verifier , and Selector maintain the evidence graph and return the target-shaped subgraph as the next context. An append-only Long History Memory and a rewritten Short Graph Memory back the loop.
Figure 3: Difficulty is controllable and empirical only for GRADS: delivered depth (bottom row) and solver score (top row) by requested band. For more details, check Appendix Table 1 .
Figure 4: SFT on GRADS data (Ours) dominates every synthesis baseline in every domain; dashed line is the raw model. Check more details in Appendix Table 5 .
Figure 5
Figure 7: Every capability tier degrades from Easy → Hard; capability is a level shift. For more details, check Appendix Table 4 .
Appendix figures & tables13 assets
Supplementary material from the paper’s appendix.
Appendix
Solver score
Reasoning depth
Method
Domain
Easy
Medium
Hard
Easy
Medium
Hard
GRADS
GitHub
0.778
0.722
0.522
3.53
5.50
11.03
Physics
0.715
0.450
0.374
3.47
4.83
10.82
Security
0.725
0.671
0.559
2.57
4.17
9.10
TaskCraft
GitHub
1.000
0.950
0.950
1.20
1.80
2.55
Physics
0.800
1.000
0.950
1.05
1.40
1.45
Appendix
Table 1: Data behind Figure 3 . Solver score (rubric-weighted, ∈[0,1] ; n=20 per cell) and delivered reasoning depth (longest necessary-inference chain) by method × domain × requested band. Only GRADS shows a monotone Easy > Medium > Hard solver-score gradient together with depth that grows Easy → Hard.
Axis
Symbol
GitHub
Physics
Security
Pooled
Depth
d(G)
−0.50
−0.51
−0.49
−0.45
Information flow
cinfo(G)
−0.38
−0.36
−0.39
−0.39
Size
∣V∣
−0.26
−0.26
−0.35
−0.38
Breadth
b(G)
−0.12
−0.07
−0.10
−0.17
Dispersion
–
−0.44
−0.34
−0.44
−0.38
Appendix
Table 2: Spearman rs between rubric-graded solver score and each term of C(G) , per domain and pooled. Depth is the strongest predictor on GitHub, Physics, and pooled; dispersion is the number of distinct source records.
Domain
Arm
Ckpt 1
Ckpt 2
Ckpt 3
Best
Δ raw
Δ ours
Physics
raw Qwen3.5-4B
–
0.2319
–
–
Ours
0.2932 (10)
0.3390 (20)
0.3139 (24)
0.3390
+0.1071
–
Claude-Code
0.2936 (12)
0.2884 (20)
0.3011 (24)
0.3011
+0.0692
−0.0379
TaskCraft
0.2507 (12)
0.2399 (20)
0.2407 (24)
0.2507
+0.0188
−0.0883
WebShaper
0.2850 (12)
0.2602 (16)
0.2548 (20)
0.2850
+0.0531
−0.0540
GitHub
raw Qwen3.5-4B
–
0.1149
–
–
Appendix
Table 3: SFT benchmark score at three checkpoints per arm (step in parentheses; best in bold), with the best score and its change versus the raw model and versus ours. Ours is the best arm in every domain.
Model
Domain
Easy
Medium
Hard
Opus-4.8 (frontier)
GitHub
0.778
0.722
0.522
Physics
0.715
0.450
0.374
Security
0.725
0.671
0.559
Sonnet-4 (medium)
GitHub
0.421
0.362
0.183
Physics
0.381
0.279
0.170
Security
0.446
0.340
0.266
Appendix
Table 4: Data behind Figure 7 . Mean solver score ( ∈[0,1] , claim-weighted judge held constant at Opus 4.8) by model × domain × band, for a frontier (Opus 4.8), medium (Sonnet 4), and light (Qwen3.5-4B) solver. Every model degrades monotonically Easy > Medium > Hard in every domain.
Domain
Arm
Ckpt 1
Ckpt 2
Ckpt 3
Physics
Ours
0.2932
0.3390
0.3139
ClaudeCode
0.2936
0.2884
0.3011
TaskCraft
0.2507
0.2399
0.2407
WebShaper
0.2850
0.2602
0.2548
GitHub
Ours
0.2574
0.2835
0.2838
ClaudeCode
0.1372
0.1202
0.1586
Appendix
Table 5: Data behind Figure 4 . Benchmark score at three checkpoints per arm and domain (dash where an arm has fewer saved points). Raw Qwen3.5-4B: Physics 0.2319 , GitHub 0.1149 , Security 0.1414 .
Domain
step 9
step 18
step 24
step 30
SFT start
raw
Physics
0.3130
0.3226
0.3185
0.3407
0.3390
0.2319
GitHub
0.2540
0.2732
0.2855
0.3069
0.2835
0.1149
Security
0.4119
0.4488
0.4663
0.4543
0.4277
0.1414
Appendix
Table 6: GRPO benchmark score at four checkpoints (steps 9/18/24/30 ), the SFT starting policy, and the raw model, behind Figure 6 . Best-RL vs. best-SFT deltas all fall inside the ±0.043 noise floor.
Model
GitHub
Physics
Security
Sonnet-4 (mid solver)
0.308
0.266
0.342
Post-trained 4B (best RL)
0.307
0.341
0.466
Δ (4B − Sonnet-4)
−0.001
+0.075
+0.124
Appendix
Table 7: Post-training closes the capability gap to a mid-sized solver. Benchmark mean score ( 30 / 30 / 40 -weighted) of the Sonnet-4 solver (from Table 4 ) versus the post-trained Qwen3.5-4B (best RL checkpoint, Table 6 ). After post-training the 4B matches Sonnet-4 on GitHub and surpasses it on Physics and Security.
Domain
Model
Tool calls
Turns
boxed
Ans. length
Cap-hits
Physics
raw
16.35
17.08
6
6662
28
SFT
10.30
11.27
97
5528
4
RL
8.29
9.29
100
6656
–
GitHub
raw
21.96
22.27
5
3172
69
SFT
10.00
10.95
95
5776
5
RL
8.95
9.94
99
5465
–
Appendix
Table 8: Data behind Figure 6 and the tool analysis. Per-item means for the raw model, best SFT, and best RL checkpoint; \boxed{} count and items-at-25-turn-cap are counts out of 100. SFT’s invariant effect is the jump in boxed rate and the drop in tool calls/turns; answer length moves in domain-specific directions.
Band
API calls
Input tok
Output tok
Total tok
Easy
17.5
149 k
31 k
180 k
Medium
30.2
271 k
56 k
327 k
Hard
40.3
358 k
76 k
434 k
Appendix
Table 9: GRADS synthesis cost per delivered item, by requested difficulty (Opus 4.8). Both API calls and tokens rise monotonically with depth.
Method
API calls / item
Tokens / item
GRADS
28
302 k
Claude-Code
33.7
236 k
TaskCraft
∼278
>0.84 M (floor)
WebShaper
∼2.9
∼22 k (est.)
Appendix
Table 10: Synthesis cost per item, GRADS versus baselines. Cost is only meaningful alongside delivered depth (Table 1 ): WebShaper is cheapest because it barely explores (and stays shallow), TaskCraft is by far the most expensive yet still shallow, and Claude-Code spends about as much as GRADS but never reaches the target depth. Measurement notes: GRADS (Opus 4.8) and Claude-Code are exact; TaskCraft calls are exact but its tokens are a floor (metered on ReAct agent-steps only, ∼3.5 k of ∼25.6 k calls, so true tokens are higher); WebShaper calls are exact but its tokens are estimated.
Type
Intent
information_lookup
Retrieve a specific grounded fact.
troubleshooting
Diagnose a symptom to its root cause.
how_to_process
Recover a procedure or sequence of steps.
summarization_analysis
Summarize and analyze a body of evidence.
domain_knowledge
Apply domain-specific concepts to the records.
code_explanation
Explain what a piece of code or a change does.
Appendix
Table 11: The ten question types and their intent.
Domain
Arm
N
Steps
Tokens (k)
Sup. tokens (k)
Turns
Tool calls
Score
Physics
Raw Qwen3.5-4B
–
–
–
–
–
–
0.2319
Ours
61
24
905.1
169.0
8.22
7.22
0.3390
ClaudeCode
59
24
938.6
156.4
10.22
9.22
0.3011
TaskCraft
60
24
626.6
84.5
4.98
3.98
0.2507
WebShaper
60
24
715.2
98.5
5.43
4.43
0.2850
GitHub
Raw Qwen3.5-4B
–
–
–
–
–
–
0.1149
Appendix
Table 12: SFT training data and benchmark score per arm, for the same arms as Table 3 . N : trajectories per arm; Steps: optimizer steps (6 epochs, 16 sequences per step); Tokens: all tokens per epoch; Sup. tokens: tokens receiving loss; Turns / Tool calls: means per trajectory; Score: reported checkpoint. † Built against an augmented retrieval index, because the method’s security questions cite documents it generated itself, so its score is a lower bound.
Setting
Value
Initialization
SFT ckpt. (physics 20, GitHub 56, security 16)
Training / validation pairs
240 / 40 per domain (in-loop validation: seeded 10-item subset)
Table 13: GRPO configuration, shared across domains except where noted. Training truncates observations to 3,000 characters and allows 12–14 turns, versus 12,000 characters and 25 turns at evaluation.
Training LLM agents for policy-constrained tool environments, such as an airline backend with booking, refund, and cancellation policies, requires tasks that exercise the environment's rules individually and in combination. Existing synthesis pipelines let a model propose tasks and retain only those it immediately solves, biasing training data toward easier behaviors. We introduce Gradus (Graded Rungs of Ascending Difficulty from an Underlying System), which decouples what a task should teach from the concrete task and solution used to teach it. Without seed tasks, Gradus treats a fixed environment as a latent syllabus, extracting guarded behavioral operators that capture how actions depend on current conditions and how their effects reshape subsequent decisions. It composes these operators into increasingly demanding tasks while using corpus-level feedback to target missing outcome combinations. Crucially, Gradus jointly calibrates tasks and solutions against the environment: execution feedback identifies which side to refine while preserving the intended behavioral requirements. Verification thus becomes a recovery signal rather than a terminal filter, coupling coverage-guided exploration with the preservation of difficult examples. From off-the-shelf backbones, the resulting curriculum lifts a 30B model from 59% to 89% on τ2-Bench and from 47% to 83% on CAR-Bench with an Opus 4.5 proposer, and to 76% and 62%, respectively, with an open-weight Qwen3-235B proposer. These results show the value of jointly controlling which behaviors are explored and which survive into verified training data.
Qing Ping, Wei Yang, Panpan Xu +4
AWS AI · University of Southern California · BMW Group Research and Technology
Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information. However, frontier systems remain proprietary, while existing open agents often generalize poorly across different task types, leaving unclear how to train a broadly capable deep research agent. We release QUEST, a family of open models (ranging from 2B to 35B) that serve as general-purpose deep research agents designed to handle a wide range of long-horizon search tasks, with strong capabilities in fact seeking, citation grounding, and report synthesis. To build QUEST, we propose an effective training recipe combining mid-training, supervised fine-tuning, and reinforcement learning. Central to this recipe is a curated data synthesis pipeline based on unified rubric trees, which applies to different task types and enables synthesizing training data with verifiable rewards without human annotation. In addition, QUEST incorporates a built-in context management mechanism that enables effective long-horizon reasoning and knowledge synthesis. Using only 8K synthesized tasks, QUEST approaches or even surpasses frontier closed-source agents across eight deep research benchmarks spanning diverse task types, and achieves the best overall performance among recent open-weight agents. We released everything: models, data, and training scripts.
Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-property restrictions, and difficulty verifying open-ended findings without known generative truth. Our core insight is to specify the world behind the files before generating the files themselves. We introduce Data World, a multi-agent engine that synthesizes environments, tasks, and rubrics from explicit mechanisms. Independent audits check target recoverability from observed files; rubrics support evaluation and RL rewards. Human preference-based curation for question realism selects 100 tasks across 50 held-out worlds and 16 domains for DataWorld-Bench. The engine also yields 2,135 training tasks from 1,037 disjoint worlds. Evaluation of ten frontier models shows that the benchmark remains challenging, particularly for insight discovery, with a consistent computation-insight gap. RL-zero training on the synthesized tasks produces DataWorld-35B-A3B from Qwen3.6-35B-A3B without trajectory distillation. Under matched evaluation, it improves over its base by 7.06 points on DataWorld-Bench, 14.28 on DAComp, 20.90 on DABstep-Hard, and 12.13 on JobBench, a broader occupational benchmark.
Zhanli Li, Zeyuan Xu, Chaoxu Pang +16
Longcat Interaction Team, Meituan · Zhongnan University of Economics and Law · Hunan University +7