VertexResearch/ultrachat-qwen3-4b-think

UltraChat reasoning traces — Qwen3-4B

Chain-of-thought traces written by Qwen3-4B with thinking enabled, answering questions from UltraChat. Each row pairs a question with the model's full <think>…</think> reasoning and the answer that followed it.

Intended as training data for distilling reasoning behaviour and chat format into small models.

A row

{
  "id": "12",
  "teacher": "Qwen3-4B",
  "problem": "How does this textbook compare to other textbooks on the same subject matter?",
  "trace": "<think>\nOkay, the user is asking how a specific textbook compares to others on the same subject. But wait, they didn't mention the textbook's title or author. Hmm, I need to figure out how to respond without that information. Maybe I should ask them to provide the name of the textbook. …\n</think>",
  "model_answer": "To provide a meaningful comparison, I would need to know the **title, author, or specific subject matter** of the textbook you're referring to. Different textbooks approach the same subject from varying angles, such as: - **Depth of coverage** (e.g., more theoretical vs. …",
  "tokens_qwen3": 522
}

Contents

rows	3,500
total tokens	5,370,009
├─ reasoning traces	2,516,226
├─ answers	2,802,442
└─ questions	51,341
tokens per row — mean / median / p90 / max	1,534 / 1,526 / 1,965 / 3,024
mean trace / answer length	719 / 801 tokens

Token counts use the Qwen3 tokenizer (Qwen/Qwen3-0.6B, shared by every Qwen3 size) with add_special_tokens=False. Each row stores tokens_qwen3, the combined length of question + trace + answer — i.e. what a fine-tune actually trains on — so you can filter by length without re-tokenising.

Usage

from datasets import load_dataset

ds = load_dataset("VertexResearch/ultrachat-qwen3-4b-think", split="train")

data/train.parquet and data/train.jsonl hold identical rows.

For reasoning-style fine-tuning, use trace + model_answer as the assistant turn. For answer-only fine-tuning, use model_answer alone.

Columns

column	type	description
id	string	UltraChat conversation id, preserved verbatim
teacher	string	model that wrote trace and model_answer
problem	string	the question — UltraChat's opening user turn
trace	string	the <think>…</think> block
model_answer	string	the answer, i.e. everything after </think>
tokens_qwen3	int	Qwen3 token count of question + trace + answer

How it was made

UltraChat rows are multi-turn conversations; only the opening user turn is used as the question, and everything after it is discarded. Each question was put to Qwen3-4B on its own, with thinking enabled and no system prompt. A response was kept only if it contained a closed thinking block followed by a non-empty answer — anything else was regenerated. That gate is why essentially every row here is structurally complete.

Quality

complete trace with a real answer after it	100.00%
trace shows self-correction ("wait", "actually", …)	92.9%
third-person narration ("the assistant should…")	1.0%
formulaic opener ("The user is asking…")	0.0%
repetition loop in the trace	0.1%

Traces are written in the first person and frequently revise themselves mid-thought, which is the behaviour worth distilling.

Which of these sets to use

These four sets exist to answer that question. A 350M base model was fine-tuned on an equal, question-aligned slice of each, and the resulting models were compared by a blind pairwise judge that scored every pairing in both presentation orders.

traces from	resulting model quality
Qwen3-0.6B	40%
Qwen3-1.7B	54%
Qwen3-4B	56%

Quality is win rate with ties counted as half; 50% is the three-way average. Sign tests on decisive results:

comparison	p	conclusion
Qwen3-0.6B vs Qwen3-1.7B	2.5%	1.7B better
Qwen3-0.6B vs Qwen3-4B	1.4%	4B better
Qwen3-1.7B vs Qwen3-4B	88%	no measurable difference

Qwen3-1.7B is the one to use. It is statistically indistinguishable from Qwen3-4B as a source of traces while being far cheaper to generate. Qwen3-0.6B is measurably worse, and its speed does not make up for it.

Limitations

1. One topic area. UltraChat's shards are topic sectors rather than a random shuffle, and these questions all come from one of them: short factual question-answering, averaging about 15 tokens. There is no long-form writing, no code, no maths, no roleplay.
2. No greetings or short turns. No row opens with "Hi" or anything like it, and almost none are under four words. A model trained only on this handles real questions well and responds oddly to social openers.
3. No system prompts, no multi-turn. Neither the generation nor these rows involve a system turn, and only the first turn of each conversation is present.
4. Factuality is bounded by the model that wrote it. The traces are fluent and structurally clean, but small models state wrong things confidently and a student will copy that. Treat this as a source of reasoning style and chat format, not of facts.
5. Synthetic and unfiltered. No toxicity, PII, or factual filtering has been applied beyond the structural check described above.

Provenance

Questions © the UltraChat authors, MIT licensed, from openbmb/UltraChat. Traces and answers generated with Qwen3, Apache 2.0. Released under MIT. Generated September 2026.