simplex-ai-inc/LiteResearcher-4B

LiteResearcher-4B

<p align="center"> <img src="assets/logo.png" alt="LiteResearcher Logo" width="400"> </p>

<p align="center"> <a href="https://simplex-ai-inc.github.io/LiteResearcher/">🌐 Project Page</a> • <a href="https://github.com/simplex-ai-inc/LiteResearcher">💻 Code</a> • <a href="https://arxiv.org/abs/2604.17931">📄 Paper</a> </p>

LiteResearcher-4B is a 4B-parameter deep research agent trained via scalable agentic reinforcement learning. Despite its small size, it matches Claude-4.5-Sonnet on GAIA and outperforms open-source models up to 8× larger.

Key Results

All with only 4B parameters — 8–32× smaller than comparable models.

Model Details

• —Architecture: Qwen3ForCausalLM (Qwen3-4B-Thinking base)
• —Parameters: 4B
• —Max Context: 262,144 tokens
• —Training: Two-stage difficulty-aware curriculum RL with virtual world environment
• —Agent Mode: ReAct-style with search and visit tools

How It Works

LiteResearcher operates as a ReAct agent that iteratively:

1. 1.Thinks about what information is needed
2. 2.Searches the web via Google
3. 3.Visits webpages to extract evidence
4. 4.Answers when sufficient information is gathered

The model uses <think>, <tool_call>, and <answer> tags to structure its reasoning.

Quick Start

With the Inference Framework

git clone https://github.com/simplex-ai-inc/LiteResearcher.git
cd LiteResearcher
pip install -r requirements.txt

# Configure API keys
cp .env.example .env
# Edit .env with your SERPER_KEY_ID and SCRAPEDO_API_KEY

# Start SGLang server
python -m sglang.launch_server \
    --model-path simplex-ai-inc/LiteResearcher-4B \
    --port 6001 --tp 2

# Run inference
bash scripts/run_all.sh \
    --model simplex-ai-inc/LiteResearcher-4B \
    --dataset data/example.jsonl

Direct Usage with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "simplex-ai-inc/LiteResearcher-4B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

messages = [
    {"role": "system", "content": "You are a deep research assistant..."},
    {"role": "user", "content": "Who won the Nobel Prize in Physics in 2024?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.6, top_p=0.95)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Training

LiteResearcher is trained with a three-component framework:

1. 1.Co-constructed Training Data & Corpus — 32M+ webpages, 1M+ domains, covering five atomic search capabilities (direct retrieval, aggregation, enumeration, cross-verification, statistics)
2. 2.Stable Local Tool Environment — Local search engine (BGE-M3 + Milvus) and local browse tool (PostgreSQL) enabling 73.2M tool calls during training at zero marginal cost
3. 3.Difficulty-Aware Curriculum RL — Multi-stage training that progressively increases task difficulty and context length

Benchmark Results

LiteResearcher-4B consistently outperforms open-source models up to 8× larger and matches or exceeds proprietary systems across eight benchmarks.

Best open-source results in bold. Results with \* use a 64k context window with a memory mechanism.

Citation

@article{li2026literesearcher,
  title={LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent},
  author={Wanli Li and Bince Qu and Bo Pan and Jianyu Zhang and Zheng Liu and Pan Zhang and Wei Chen and Bo Zhang},
  journal={arXiv preprint arXiv:2604.17931},
  year={2026}
}

License

This model is released under the Apache 2.0 License.