README.md

---
license: apache-2.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
  - zh
tags:
  - agentic-rl
  - deep-research
  - sft
  - cold-start
  - tool-use
  - reasoning
  - rag
  - trajectories
pretty_name: LiteResearcher SFT Cold-Start Trajectories
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
---

# LiteResearcher — SFT Cold-Start Data

<div align="center">

**Distilled deep-research trajectories used for the SFT cold-start of LiteResearcher-4B**

[![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2604.17931)
[![Code](https://img.shields.io/badge/Code-GitHub-181717?logo=github&logoColor=white)](https://github.com/simplex-ai-inc/LiteResearcher)
[![Webpage](https://img.shields.io/badge/Project-Webpage-0a0a0a?logo=githubpages&logoColor=white)](https://simplex-ai-inc.github.io/LiteResearcher/)
[![Model](https://img.shields.io/badge/Model-LiteResearcher--4B-ffcc00?logo=huggingface&logoColor=black)](https://huggingface.co/simplex-ai-inc/LiteResearcher-4B)
[![RL Data](https://img.shields.io/badge/RL%20Data-LiteResearcher--Data-ffcc00?logo=huggingface&logoColor=black)](https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-Data)

</div>

This dataset contains the **68,231 multi-turn deep-research trajectories** used to
train the SFT cold-start checkpoint that RL (GRPO+TIS) is later launched from —
the "68.2 K distilled deep-research trajectories" referenced in the paper and in
[`LiteResearcher-Data`](https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-Data).

Each row is a complete ReAct-style episode: a research question, the model's
interleaved thinking and `search` / `browse` tool calls, the observations that
came back, and a final `<answer>`.

> **Where this sits in the pipeline:** SFT cold-start → Stage-1 RAG warmup →
> Stage-2 curriculum RL. The RL *prompts* live in
> [`simplex-ai-inc/LiteResearcher-Data`](https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-Data);
> the webpage corpus behind the local search/browse environment lives in
> [`simplex-ai-inc/LiteResearcher-Corpus`](https://huggingface.co/datasets/simplex-ai-inc/LiteResearcher-Corpus).
> This repo is the *supervised trajectories* that teach the tool-use loop before
> any RL happens.

---

## At a glance

| | |
|---|---|
| Rows | 68,231 |
| Format | Parquet, 9 shards, ~732 MB total |
| Messages per trajectory | min 3, mean 18.3, max 197 |
| Total assistant turns | 591,304 |
| Language | ~74 % English / ~26 % Chinese (by question) |
| Context length | fits a 64 K-token training window (Qwen3 tokenizer) |

---

## Source mix

Trajectories are distilled over questions drawn from these upstream pools:

| `source` | Rows | What it is |
|---|---|---|
| `direct_information_seeking_datagen_row1-row42748_v2` | 27,744 | Synthesised direct information-seeking questions (first batch) |
| `MiroRL_GenQA` | 10,400 | Generated QA from the MiroRL question pool |
| `taskcraft` | 8,645 | TaskCraft-style compositional task questions |
| `asearcher` | 8,364 | ASearcher-style search questions |
| `direct_information_seeking_datagen_row42748-_v2` | 8,265 | Synthesised direct information-seeking questions (second batch) |
| `chinese_QA` | 2,502 | Chinese-language QA |
| `BenchSeedQA` | 2,311 | Seed questions derived from benchmark-style tasks |

The two `direct_information_seeking_*_v2` shards are the same generator run split
by row range — treat them as one 36,009-row pool if you want a coarser grouping.

---

## Schema

| Column | Type | Description |
|---|---|---|
| `conversations` | `list[{role, content}]` | The full trajectory in ShareGPT form. Roles are `system`, `user`, `assistant`. |
| `source` | string | Upstream question pool (see table above). |

**On the role encoding:** tool *observations* (search results, fetched page
content) are carried in `user` turns rather than a separate `observation` /
`tool` role — this is the ShareGPT convention the LLaMA-Factory recipe expects.
A trajectory alternates `assistant` (reasoning + tool call) ↔ `user`
(observation), ending on an `assistant` turn that emits the final answer.

The `system` turn carries the deep-research system prompt: a role description,
the `<tools>` block with JSON function signatures for `search` and `browse`, and
the requirement that the final response be wrapped in `<answer></answer>` tags.
`assistant` turns emit reasoning followed by either a `<tool_call>` or the final
`<answer>`.

---

## How to use

### With 🤗 datasets

```python
from datasets import load_dataset

ds = load_dataset("simplex-ai-inc/LiteResearcher-SFT-Data", split="train")
print(ds)                          # 68,231 rows
print(ds[0]["source"])
print(ds[0]["conversations"][0])   # system prompt (tools block)
print(ds[0]["conversations"][1])   # the research question
```

### With LLaMA-Factory

```bash
hf download simplex-ai-inc/LiteResearcher-SFT-Data --repo-type dataset --local-dir ./literesearcher_sft
```

Register it in `data/dataset_info.json`:

```json
{
  "literesearcher_sft": {
    "file_name": "literesearcher_sft/data",
    "formatting": "sharegpt",
    "columns": { "messages": "conversations" },
    "tags": {
      "role_tag": "role", "content_tag": "content",
      "user_tag": "user", "assistant_tag": "assistant",
      "observation_tag": "observation", "system_tag": "system"
    }
  }
}
```

Cold-start config we used (Qwen3-4B-Thinking-2507, 8×H100, full fine-tune):

```yaml
dataset: literesearcher_sft
template: qwen3
enable_thinking: true
cutoff_len: 65536          # 64K training window
learning_rate: 2.0e-5
num_train_epochs: 1.0
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
lr_scheduler_type: cosine
warmup_ratio: 0.1
weight_decay: 0.01
bf16: true
gradient_checkpointing: true
flash_attn: fa2
enable_liger_kernel: true
deepspeed: examples/deepspeed/ds_z2_config.json
```

---

## How it was built

1. **Question pooling** — questions were collected from the sources above,
   spanning direct information-seeking, multi-hop, compositional (TaskCraft),
   and Chinese-language QA.
2. **Trajectory distillation** — a stronger teacher model rolled out full
   ReAct episodes against the search / browse environment, producing interleaved
   reasoning, tool calls, and observations.
3. **Cleaning** — trajectories were filtered for a well-formed output contract
   (`<tool_call>` / `<answer>`), consistent tool-call syntax, answer agreement
   with the reference, and removal of degenerate repeated-action episodes.
4. **Length check** — remaining episodes were tokenised with the Qwen3 tokenizer
   and verified against the 64 K context window.

See §3 and §5 of the [paper](https://arxiv.org/abs/2604.17931) for how the
cold-start checkpoint feeds into the two RL stages.

---

## Limitations & responsible use

- Trajectories are **teacher-distilled, not human-verified**. Intermediate
  reasoning can contain factual errors, dead-end searches, or hallucinated
  intermediate claims even when the final answer matches the reference. Treat
  this as behaviour-cloning data for the *tool-use loop*, not as a source of
  ground truth.
- Observations are **snapshots of live web content** from the collection period
  (late 2025). Some pages will have changed or disappeared; some contain
  public-facing contact details that were already indexed on the open web.
- No held-out split is bundled. Evaluate on standard deep-research benchmarks
  (GAIA, Xbench-DS, Frames, BrowseComp, HLE, Seal-0, WebWalkerQA) via the
  [`Inference/`](https://github.com/simplex-ai-inc/LiteResearcher/tree/main/Inference)
  harness.

If you spot a row that shouldn't be public, please open an issue on the
[GitHub repo](https://github.com/simplex-ai-inc/LiteResearcher/issues).

---

## License

Released under **Apache-2.0**, matching the code and the RL data release.

---

## Citation

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