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Model: simplex-ai-inc/LiteResearcher-4B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen3-4B-Thinking
+tags:
+- deep-research
+- react-agent
+- reinforcement-learning
+- search-agent
+- agentic-rl
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# 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
+
+| Benchmark | LiteResearcher-4B | Notable Comparison |
+|---|---|---|
+| **GAIA-Text** | **71.3%** | = Claude-4.5-Sonnet (71.2%) |
+| **Xbench-DS** | **78.0%** | > Tongyi DeepSearch 30B (75.0%) |
+| **Frames** | **83.1%** | > Claude-4-Sonnet (80.7%) |
+| **WebWalkerQA** | **72.7%** | > Tongyi DeepSearch 30B (72.2%) |
+
+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. **Thinks** about what information is needed
+2. **Searches** the web via Google
+3. **Visits** webpages to extract evidence
+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
+
+```bash
+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
+
+```python
+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. **Co-constructed Training Data & Corpus** — 32M+ webpages, 1M+ domains, covering five atomic search capabilities (direct retrieval, aggregation, enumeration, cross-verification, statistics)
+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. **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.
+
+| Model | Size | GAIA | BrowseComp (en) | BrowseComp (zh) | Humanity | Frames | WebWalkerQA | MAIA | Xbench-DS |
+|---|---|---|---|---|---|---|---|---|---|
+| | | | | **Commercial Models** | | | | | |
+| Claude-4-Sonnet | - | 68.3 | 12.2 | 29.1 | 20.3 | 80.7 | 61.7 | - | 64.6 |
+| Claude-4.5-Sonnet | - | 71.2 | 19.6 | 40.8 | 24.5 | 85.0 | - | 53.4 | 66.0 |
+| DeepSeek-V3.2 | - | 63.5 | 67.6 | 65.0 | 40.8 | 80.2 | - | 38.5 | 71.0 |
+| DeepSeek-V3.1 | - | 63.1 | 30.0 | 49.2 | 29.8 | 83.7 | 61.2 | - | 71.0 |
+| Minimax-M2 | - | 75.7 | 44.0 | 48.5 | 31.8 | - | - | - | 72.0 |
+| OpenAI-GPT-5-high | - | 76.4 | 54.9 | 65.0 | 35.2 | - | - | 51.4 | 77.8 |
+| GLM-4.6 | - | 71.9 | 45.1 | 49.5 | 30.4 | - | - | - | 70.0 |
+| Kimi-Researcher | - | - | - | - | 26.9 | 78.8 | - | 36.0 | 69.0 |
+| Kimi-K2-0905 | - | 60.2 | 7.4 | 22.2 | 21.7 | 58.1 | - | 25.2 | 61.0 |
+| | | | | **Open-Source Models** | | | | | |
+| Mirothinker | 8B | 66.4 | 31.1 | 40.2 | 21.5 | 80.6 | 60.6 | 40.4 | 60.6 |
+| Tongyi DeepSearch | 30B | 70.9 | 43.4 | 46.7 | 32.9 | **90.6** | 72.2 | - | 75.0 |
+| ASearcher QWQ v2 | 32B | 58.7 | - | - | - | 74.5 | - | - | 51.1 |
+| WebSailor | 30B | 53.2 | - | - | - | - | - | - | 53.3 |
+| WebDancer (QwQ) | 32B | 51.5 | 3.8 | 18.0 | - | - | 47.9 | - | 38.3 |
+| WebExplorer | 8B | 50.0 | 15.7 | 32.0 | 17.3 | 75.7 | 62.7 | - | 53.7 |
+| DeepMiner | 32B | 58.7 | 33.5 | 40.1 | - | - | - | - | 62.0 |
+| AFM-RL | 32B | 55.3 | 11.1 | - | 18.0 | - | 63.0 | - | - |
+| SFR-DeepResearch | 20B | 66.0 | - | - | 28.7 | 82.8 | - | - | - |
+| AgentCPM-Explore | 4B | 63.9 | 24.1 | 29.1 | 19.1 | 82.7 | 68.1 | 40.5 | 70.0 |
+| **LiteResearcher** | **4B** | **71.3** | 27.5\* | 32.5\* | 22.0 | 83.1 | **72.7** | **41.8** | **78.0** |
+
+Best open-source results in **bold**. Results with \* use a 64k context window with a memory mechanism.
+
+## Citation
+
+```bibtex
+@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](https://www.apache.org/licenses/LICENSE-2.0).

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
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+  "<|file_sep|>": 151664,
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+  "<|image_pad|>": 151655,
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+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n<think>\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.1",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.56.1"
+}

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