QUEST-4B
QUEST 4B SFT model — general-purpose deep research agent (Qwen3.5 family, dense).
Benchmark results
| Benchmark | Metric | Score |
|---|---|---|
| BrowseComp | avg@3 | 40.0 |
| Mind2Web 2 | avg@3 | 24.3 |
| HLE | avg@3 | 36.2 |
| DeepResearch Bench | avg@3 | 22.0 |
| BrowseComp-Plus | avg@3 | 52.1 |
| WideSearch | Item F1 avg@4 | 55.0 |
| GAIA | avg@3 | 77.7 |
| LiveResearchBench | avg@3 | 62.1 |
QUEST Family
| Type | Resources |
|---|---|
| 35B checkpoints | RL, MT+SFT, MT, SFT |
| 30B checkpoints | RL, MT+SFT, SFT |
| Smaller checkpoints | 9B, 4B, 2B |
| Training data | RL data, SFT objective data, SFT open-ended data, Mid-training data |
Model selection note: if you only need to evaluate objective tasks and do not need open-ended task evaluation, we recommend the MT+SFT checkpoints because they perform better on reasoning-heavy objective benchmarks. For a more comprehensive evaluation across both objective and open-ended tasks, we recommend the RL checkpoints.
Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "osunlp/QUEST-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, device_map="auto", torch_dtype="auto",
)
Apply the model's chat template with tokenizer.apply_chat_template(...) before passing prompts.
License
Released under the Apache License 2.0.
Citation
If our paper or related resources prove valuable to your research, we kindly ask for a citation.
@misc{xie2026quest,
title={QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks},
author={Xie, Jian and Lin, Tianhe and Wang, Zilu and Ning, Yuting and Yao, Yuekun and Xue, Tianci and Zhang, Zhehao and Li, Zhongyang and Zhang, Kai and Wu, Yufan and Chen, Shijie and Gou, Boyu and Han, Mingzhe and Wang, Yifei and Lee, Vint and Wei, Xinpeng and Wang, Xiangjun and Su, Yu and Sun, Huan},
journal={arXiv preprint arXiv:2605.24218},
year={2026}
}