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osunlp
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
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osunlp
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Input Modalities
Output Modalities
Supported Functionality
| Benchmark | Metric | Score |
|---|---|---|
| BrowseComp | avg@3 | 45.4 |
| Mind2Web 2 | avg@3 | 24.4 |
| HLE | avg@3 | 36.9 |
| DeepResearch Bench | avg@3 | 32.6 |
| BrowseComp-Plus | avg@3 | 55.6 |
| WideSearch | Item F1 avg@4 | 58.5 |
| GAIA | avg@3 | 78.6 |
| LiveResearchBench | avg@3 | 63.5 |
| Type | Resources |
|---|---|
| 35B checkpoints | RL, MT+SFT, MT, SFT |
| 30B checkpoints | RL, MT+SFT, SFT |
| Smaller checkpoints | , , |
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.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "osunlp/QUEST-9B"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.
Released under the Apache License 2.0.
If our paper or related resources prove valuable to your research, we kindly ask for a citation.
bibtex
@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}}
| Training data | RL data, SFT objective data, SFT open-ended data, Mid-training data |