---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
language:
- en
- zh
base_model:
- Qwen/Qwen3-4B-Thinking-2507
tags:
- agent
- deep-research
- react
- search
- sft
- cold-start
- thinking
- LiteResearcher
datasets:
- simplex-ai-inc/LiteResearcher-Data
---

# LiteResearcher-4B-SFT

This is the **SFT cold-start checkpoint** for [LiteResearcher](https://github.com/simplex-ai-inc/LiteResearcher) —
a scalable agentic RL training framework for deep-research agents.

It is the initial policy used to launch the two-stage curriculum RL training that
produces the final [`simplex-ai-inc/LiteResearcher-4B`](https://huggingface.co/simplex-ai-inc/LiteResearcher-4B) model.

> If you are looking for the **final RL** model, please use
> [`simplex-ai-inc/LiteResearcher-4B`](https://huggingface.co/simplex-ai-inc/LiteResearcher-4B).
> If you want to **reproduce the RL training** from scratch, this is the checkpoint you need.

## Model details

- **Base model**: [`Qwen/Qwen3-4B-Thinking-2507`](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
- **Architecture**: `Qwen3ForCausalLM` (36 layers, hidden 2560, 32 heads, GQA 8 KV heads)
- **Max position embeddings**: 262,144 (RoPE θ = 5,000,000)
- **Precision**: `bfloat16`
- **Total params**: ~4B
- **Training framework**: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)

## Training recipe

| Item | Value |
|---|---|
| Stage | SFT (cold-start before RL) |
| Base model | `Qwen/Qwen3-4B-Thinking-2507` |
| Dataset | [`simplex-ai-inc/LiteResearcher-Data`](https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-Data) (~68.2k SFT trajectories) |
| Max sequence length | 64K (`cutoff_len=65536`) |
| Global batch size | 128 (per-device bs 2 × grad-accum 8 × 8 GPUs) |
| Epochs | 1 |
| Optimizer steps | 533 |
| Learning rate | 2.0e-5, cosine, 10% warmup |
| Final train loss | ≈ 0.447 (starting loss ≈ 1.19) |

The SFT trajectories teach the model the ReAct `think` → `search` → `visit` → `answer`
loop and the strict `<answer>...</answer>` output contract used by the RL environment.
Because the base is the **Thinking-2507** variant, the model preserves long
chain-of-thought behavior inside `<think>...</think>` blocks, which is what the
downstream RL curriculum builds on.

## How to use

### As the initial policy for RL (recommended use)

```bash
# In the LiteResearcher training scripts (Training/ folder of the repo)
export MODEL_PATH=$(hf download simplex-ai-inc/LiteResearcher-4B-SFT \
                                --local-dir ./literesearcher_sft)
```

Then follow the Stage-1 / Stage-2 RL instructions in the
[LiteResearcher repository](https://github.com/simplex-ai-inc/LiteResearcher).

### Stand-alone inference

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "simplex-ai-inc/LiteResearcher-4B-SFT"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)
```

The model expects the same ReAct system prompt and tool schema used by
LiteResearcher (see `Inference/` in the repo).

## Citation

If you use this checkpoint in academic work, please cite the LiteResearcher
project — see the [GitHub README](https://github.com/simplex-ai-inc/LiteResearcher) for the BibTeX entry.

## License

Apache-2.0, inheriting from the [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) base model.