Trains deep search agents from knowledge graphs

https://t.co/EgfeW3uJX0 https://t.co/kX5Wdusr7R

THUDM/DeepDive

Source: https://github.com/THUDM/DeepDive

DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL

🔥 News

• [2025/10/02] Released the complete data construction pipeline — now fully available in the repository.
• [2025/09/17] QA pairs and SFT trajectories have been fully open-sourced, totaling 4,108 entries. Check them out on Hugging Face Dataset DeepDive.
• Model and code are currently under development – coming soon!

Overview

DeepDive presents an automated approach for training deep search agents that can navigate complex, multi-step information-seeking tasks. Our method combines automated data synthesis from knowledge graphs with end-to-end multi-turn reinforcement learning to create agents capable of sophisticated long-horizon reasoning and web browsing.

Key Features

• Automated Deep Search Data Synthesis: Generate challenging QA pairs from knowledge graphs through controlled random walks
• Multi-Turn RL Training for Browsing: End-to-end reinforcement learning for deep search capabilities
• Test-Time Scaling: Supports scaling via tool calls and parallel sampling

Method Overview

Stage 1: Automated Data Synthesis from Knowledge Graphs

We propose an automated method to synthesize complex, difficult, and hard-to-find questions from open knowledge graphs. The process involves three key steps:

Knowledge Graph Random Walks: Starting from an initial node v_0, we navigate through the graph for k steps to form a path P=[v_0, v_1, \ldots, v_k], where each step (v_i, v_{i+1}) is a valid edge in the graph. We choose longer path lengths (k > 5) to increase reasoning complexity.

Entity Obfuscation: We combine each node v_i in the path with its corresponding attributes to form an attribute-rich path:

P_A = [(v_0, [a_0^0, a_0^1, \ldots]), (v_1, [a_1^0, a_1^1, \ldots]), \ldots, (v_k, [a_k^0, a_k^1, \ldots])]

An LLM then obfuscates information along the entire path, generalizing specific details and creating “blurry entities” that require deep search to resolve.

Difficulty Filtering: We use a frontier model (GPT-4o) with basic search to attempt each question four times. Only questions that the frontier model fails in all attempts are retained, ensuring high difficulty.

Stage 2: End-to-End Multi-Turn Reinforcement Learning

We apply end-to-end multi-turn RL to enhance the agent’s long-horizon reasoning and browsing capabilities. The training process follows an iterative cycle where at step t, the agent generates chain-of-thought c_t, executes browsing action a_t, and observes web content o_t.

Multi-Turn GRPO Training: We employ Group Relative Policy Optimization with normalized advantages:

A_i = \frac{r_i - \text{mean}(\{r_k\}_{k=1}^G)}{\text{std}(\{r_k\}_{k=1}^G)}

Strict Binary Rewards: A trajectory receives reward +1 if and only if both format correctness and answer accuracy are satisfied:

r(\mathcal{T}) = \begin{cases} 1, & (\forall i, \text{Format}(c_i, a_i)) \wedge \text{Judge}(a_{\text{eos}}, a^*) \\ 0, & \text{otherwise} \end{cases}

This strict reward mechanism ensures high-quality trajectories and prevents reward hacking.

Models

Data

Synthetic Dataset Construction

Our automated data synthesis pipeline creates challenging QA pairs through knowledge graph random walks, entity obfuscation, and difficulty filtering. The process uses multi-hop paths (k=5-9) through KILT and AMiner knowledge graphs.

Data Example

Results

Main Results

We evaluate DeepDive on four challenging deep search benchmarks: BrowseComp, BrowseComp-ZH, Xbench-DeepSearch, and SEAL-0. The results demonstrate consistent improvements over existing open-source models.

* represents reported performance from existing studies. † represents equipping browsing via function call.

Generalization on Simple Search Tasks

We evaluate DeepDive not only on challenging search tasks (e.g., BrowseComp, BrowseComp-ZH) but also on simpler benchmarks such as HotpotQA, Frames, and WebWalker. DeepDive-32B (SFT and RL) consistently outperforms strong baselines, achieving >60 points on WebWalker, surpassing WebShaper-72B (52.2). These results demonstrate DeepDive’s strong and generalizable search capabilities.

Test-Time Scaling

DeepDive demonstrates remarkable test-time scaling capabilities through two mechanisms:

Tool Call Scaling: Allowing DeepDive more tool calls during inference leads to higher accuracy on complex, multi-step tasks. As shown in the BrowseComp and BrowseComp-ZH benchmark results:

• When the maximum number of tool calls is increased, accuracy rises steadily. On BrowseComp, performance improves from 8% accuracy at 8 tool calls to 15% at 128 tool calls, showing that the model benefits from additional search and reasoning opportunities.
• DeepDive-32B consistently outperforms its SFT-only variant, especially when the allowed tool calls exceed 32. This indicates that the reinforcement learning stage better equips the model to utilize long tool call horizons.

Parallel Sampling: Beyond a larger tool call budget, DeepDive leverages parallel sampling to further boost performance. For each question, DeepDive generates 8 independent reasoning trajectories in parallel.

• Three answer selection strategies are considered: single-shot inference, majority voting among samples, and choosing the answer that required the fewest tool calls before submission.
• Empirical analysis reveals a clear trend: answers submitted earlier with fewer tool calls are usually more accurate. In practice, selecting the answer with minimal tool calls among 8 samples raises accuracy from 12.0% (single-shot) to 24.8%, more than doubling performance. Majority voting also helps (18.8% accuracy), but is outperformed by minimal tool-call selection.

Additional Study: Semi-Automated i.i.d. Deep Search QA for RL

To further boost performance on deep search tasks, we create a semi-automated framework for generating i.i.d. QA pairs.

Training with i.i.d. data brings larger improvements. The 32B-RL model reaches 22.2% accuracy on BrowseComp, up from 14.8%, and also performs better on Chinese benchmarks.

Given the contamination analysis, both the KG data and i.i.d. data are adopted by the open  GLM-4.5 / GLM-4.6 models, which show strong performance on BrowseComp.

Acknowledgments

• Built on top of GLM-4 and QwQ base models
• Uses Slime framework for RL training
• Powered by Serper and Jina APIs for web access

Citation

If you find DeepDive useful for your research, please cite our paper:

@misc{lu2025deepdiveadvancingdeepsearch,
      title={DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL},
      author={Rui Lu and Zhenyu Hou and Zihan Wang and Hanchen Zhang and Xiao Liu and Yujiang Li and Shi Feng and Jie Tang and Yuxiao Dong},
      year={2025},
      eprint={2509.10446},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.10446},
}