Datasets:

SecondLookResearch
/

teaching-qwen-why-data

---
license: cc-by-4.0
language:
- en
pretty_name: Teaching Qwen Why — data
tags:
- alignment
- synthetic
- fiction
viewer: false
---
# Teaching Qwen Why — data

The data behind **Teaching Qwen Why**, a replication and extension of
Anthropic's "Teaching Claude Why" on Qwen 2.5 32B and 72B, by Anastasia Wei,
Jack Thompson, Brandon Qi and Stewart Slocum (Second Look Research).

Code: [uchicago-xlab/TeachingClaudeWhyReplication](https://github.com/uchicago-xlab/TeachingClaudeWhyReplication).
The folders here match the code repository's `data/` folder, so you can
download straight into it:

```bash
hf download SecondLookResearch/teaching-qwen-why-data --repo-type dataset --local-dir data
```

## Contents

| Folder | What it holds | Used in |
|---|---|---|
| `stories/training/` | Story corpora as trained on (one story per row). 14M Qwen 2.5 tokens each unless the name gives another size. | Figures 1–4, 6, 7, 13 |
| `stories/generated/` | Every kept story with its prompt metadata (constitution section, genre, setting, point of view, teacher settings), and the protagonist rewrites. | — |
| `difficult_advice/<dataset>/` | `train*.jsonl` and `val.jsonl` in the Qwen chat format, `ft_dataset.jsonl` (the same conversations, model-neutral) and `critiqued_prompts.json` (every pipeline stage's output). | Figures 1–3, 9–12, 14 |
| `difficult_advice/decline-judge/` | Decline labels for the refusal-control training data. | Figure 10 |
| `eval_scores/` | `conditions.csv` (one row per eval condition) and `samples.csv` (one row per graded sample: grader verdict and whether the model acted), for every eval run in the post. No transcripts. `refusal-judge/` holds the refusal-control eval labels. | All figures except 8, 9, 15 |
| `constitution/constitution.md` | The constitution text the data was generated from, with "Claude" and "Anthropic" replaced by `[MODEL]` and `[COMPANY]`. | Data generation |

## Names

Story corpora are named `stories-<teacher>[-recitation|-human|-named-claude|-named-qwen][-<size>]`.
Difficult-advice datasets are named `<response writer>[-<prompt writer>-prompts]-<conversations>`;
`gpt-5.6-terra-3000` holds nested training sets of 500, 1,000, 2,000 and 3,000
conversations drawn from one shuffle, sharing one validation set. Eval runs in
`eval_scores/` are named `<model>__as-<address name>[-human-persona]__<conditions>x<samples>`,
where the model name lists its training stages (see the code repository's README).

Teachers: Claude Sonnet 5 (`sonnet-5`), Claude Haiku 4.5 (`haiku-4.5`),
GPT-5.4 nano (`gpt-5.4-nano`), GPT-5.6 Terra (`gpt-5.6-terra`). The protagonist
rewrites were made by GPT-5.4.

## Notes

- **Instruct mix.** The 13K instruction-tuning mix is not included because one
  of its sources (No Robots) is licensed CC BY-NC 4.0. Rebuild it with
  `data_generation/instruct_mix/build_mix.py` in the code repository.
- **Recitation corpora.** The two `-recitation` corpora were cut by an earlier
  version of the corpus script that did not remove duplicate stories. Use these
  files, not a rebuild, to reproduce Figure 4.
- **`gpt-5.6-terra-285`** was validated on the 15 held-out conversations of
  `gpt-5.6-terra-135`; its `val.jsonl` is that file.
- **Qwen3-14B refusal control** (Appendix B.2, Figures 10 and 11): `sonnet-5-first`
  and `gpt-5.6-terra-first` are the standard datasets, written with the first
  response critique; the `-refusal` folders are their refusal versions, with the
  same prompts. `gpt-5.6-terra-sonnet-5-prompts-first` and its `-refusal` twin are
  Terra's answers to the Sonnet prompts, used for the decline rates in Figure 10.
- **Qwen3-14B teacher comparison** (Appendix B.1, Figure 9): one dataset per
  teacher, written with the first response critique: `opus-4.8-first`,
  `sonnet-5-thinking-first`, `haiku-4.5-first`, `gpt-5.6-terra-first`,
  `gpt-5.4-nano-first`, `deepseek-v4-flash-first`, and
  `hybrid-sonnet-5-deepseek-v4-flash-first` (Sonnet 5 wrote the themes and
  scenarios, DeepSeek V4 Flash the rest).

## Terms of the generating models

Almost all text in this dataset was written by commercial models, through their
providers' APIs: the stories by Claude Sonnet 5 and Claude Haiku 4.5
(Anthropic) and by GPT-5.4 nano and GPT-5.6 Terra (OpenAI); the protagonist
rewrites by GPT-5.4; the difficult-advice conversations by Claude Sonnet 5 and
GPT-5.6 Terra. The decline and refusal labels were written by Claude Sonnet 5.5,
and the eval verdicts in `eval_scores/` by Claude Sonnet 4.6. This dataset
therefore includes output generated by third-party models, and that output is
subject to separate terms governing its use.

Both providers assign ownership of outputs to the customer but limit how
outputs may be used to build other models. Anthropic's
[Commercial Terms](https://www.anthropic.com/legal/commercial-terms) (effective
June 17, 2025) say a customer may not "access the Services to build a competing
product or service, including to train competing AI models ... except as
expressly approved by Anthropic". OpenAI's terms forbid using output "to
develop artificial intelligence models that compete with OpenAI's products and
services", except as their terms permit.

We release this data for alignment research: to let others reproduce and
extend our study of how training data shapes agentic misalignment in
open-weight models. We make no claim that these restrictions do not apply to
your use. Before you train models on this data, and especially before any
commercial use, read the current terms of each provider and decide whether your
use is allowed. The CC-BY-4.0 license covers our contribution (the selection,
filtering, structure, labels and score tables); it cannot grant rights that the
providers' terms withhold.

The data was generated for training and evaluation, not as reference text. The
stories are fiction, and the advice conversations were written to model a
style of reasoning; neither was checked for factual accuracy.

## Citation

```bibtex
@misc{wei2026teachingqwenwhy,
  title  = {Teaching Qwen Why},
  author = {Wei, Anastasia and Thompson, Jack and Qi, Brandon and Slocum, Stewart},
  year   = {2026},
  note   = {Second Look Research}
}
```

	---
	license: cc-by-4.0
	language:
	- en
	pretty_name: Teaching Qwen Why — data
	tags:
	- alignment
	- synthetic
	- fiction
	viewer: false
	---

	# Teaching Qwen Why — data

	The data behind Teaching Qwen Why, a replication and extension of
	Anthropic's "Teaching Claude Why" on Qwen 2.5 32B and 72B, by Anastasia Wei,
	Jack Thompson, Brandon Qi and Stewart Slocum (Second Look Research).

	Code: [uchicago-xlab/TeachingClaudeWhyReplication](https://github.com/uchicago-xlab/TeachingClaudeWhyReplication).
	The folders here match the code repository's `data/` folder, so you can
	download straight into it:

	```bash
	hf download SecondLookResearch/teaching-qwen-why-data --repo-type dataset --local-dir data
	```

	## Contents

	\| Folder \| What it holds \| Used in \|
	\|---\|---\|---\|
	\| `stories/training/` \| Story corpora as trained on (one story per row). 14M Qwen 2.5 tokens each unless the name gives another size. \| Figures 1–4, 6, 7, 13 \|
	\| `stories/generated/` \| Every kept story with its prompt metadata (constitution section, genre, setting, point of view, teacher settings), and the protagonist rewrites. \| — \|
	\| `difficult_advice/<dataset>/` \| `train*.jsonl` and `val.jsonl` in the Qwen chat format, `ft_dataset.jsonl` (the same conversations, model-neutral) and `critiqued_prompts.json` (every pipeline stage's output). \| Figures 1–3, 9–12, 14 \|
	\| `difficult_advice/decline-judge/` \| Decline labels for the refusal-control training data. \| Figure 10 \|
	\| `eval_scores/` \| `conditions.csv` (one row per eval condition) and `samples.csv` (one row per graded sample: grader verdict and whether the model acted), for every eval run in the post. No transcripts. `refusal-judge/` holds the refusal-control eval labels. \| All figures except 8, 9, 15 \|
	\| `constitution/constitution.md` \| The constitution text the data was generated from, with "Claude" and "Anthropic" replaced by `[MODEL]` and `[COMPANY]`. \| Data generation \|

	## Names

	Story corpora are named `stories-<teacher>[-recitation\|-human\|-named-claude\|-named-qwen][-<size>]`.
	Difficult-advice datasets are named `<response writer>[-<prompt writer>-prompts]-<conversations>`;
	`gpt-5.6-terra-3000` holds nested training sets of 500, 1,000, 2,000 and 3,000
	conversations drawn from one shuffle, sharing one validation set. Eval runs in
	`eval_scores/` are named `<model>__as-<address name>[-human-persona]__<conditions>x<samples>`,
	where the model name lists its training stages (see the code repository's README).

	Teachers: Claude Sonnet 5 (`sonnet-5`), Claude Haiku 4.5 (`haiku-4.5`),
	GPT-5.4 nano (`gpt-5.4-nano`), GPT-5.6 Terra (`gpt-5.6-terra`). The protagonist
	rewrites were made by GPT-5.4.

	## Notes

	- Instruct mix. The 13K instruction-tuning mix is not included because one
	of its sources (No Robots) is licensed CC BY-NC 4.0. Rebuild it with
	`data_generation/instruct_mix/build_mix.py` in the code repository.
	- Recitation corpora. The two `-recitation` corpora were cut by an earlier
	version of the corpus script that did not remove duplicate stories. Use these
	files, not a rebuild, to reproduce Figure 4.
	- `gpt-5.6-terra-285` was validated on the 15 held-out conversations of
	`gpt-5.6-terra-135`; its `val.jsonl` is that file.
	- Qwen3-14B refusal control (Appendix B.2, Figures 10 and 11): `sonnet-5-first`
	and `gpt-5.6-terra-first` are the standard datasets, written with the first
	response critique; the `-refusal` folders are their refusal versions, with the
	same prompts. `gpt-5.6-terra-sonnet-5-prompts-first` and its `-refusal` twin are
	Terra's answers to the Sonnet prompts, used for the decline rates in Figure 10.
	- Qwen3-14B teacher comparison (Appendix B.1, Figure 9): one dataset per
	teacher, written with the first response critique: `opus-4.8-first`,
	`sonnet-5-thinking-first`, `haiku-4.5-first`, `gpt-5.6-terra-first`,
	`gpt-5.4-nano-first`, `deepseek-v4-flash-first`, and
	`hybrid-sonnet-5-deepseek-v4-flash-first` (Sonnet 5 wrote the themes and
	scenarios, DeepSeek V4 Flash the rest).

	## Terms of the generating models

	Almost all text in this dataset was written by commercial models, through their
	providers' APIs: the stories by Claude Sonnet 5 and Claude Haiku 4.5
	(Anthropic) and by GPT-5.4 nano and GPT-5.6 Terra (OpenAI); the protagonist
	rewrites by GPT-5.4; the difficult-advice conversations by Claude Sonnet 5 and
	GPT-5.6 Terra. The decline and refusal labels were written by Claude Sonnet 5.5,
	and the eval verdicts in `eval_scores/` by Claude Sonnet 4.6. This dataset
	therefore includes output generated by third-party models, and that output is
	subject to separate terms governing its use.

	Both providers assign ownership of outputs to the customer but limit how
	outputs may be used to build other models. Anthropic's
	[Commercial Terms](https://www.anthropic.com/legal/commercial-terms) (effective
	June 17, 2025) say a customer may not "access the Services to build a competing
	product or service, including to train competing AI models ... except as
	expressly approved by Anthropic". OpenAI's terms forbid using output "to
	develop artificial intelligence models that compete with OpenAI's products and
	services", except as their terms permit.

	We release this data for alignment research: to let others reproduce and
	extend our study of how training data shapes agentic misalignment in
	open-weight models. We make no claim that these restrictions do not apply to
	your use. Before you train models on this data, and especially before any
	commercial use, read the current terms of each provider and decide whether your
	use is allowed. The CC-BY-4.0 license covers our contribution (the selection,
	filtering, structure, labels and score tables); it cannot grant rights that the
	providers' terms withhold.

	The data was generated for training and evaluation, not as reference text. The
	stories are fiction, and the advice conversations were written to model a
	style of reasoning; neither was checked for factual accuracy.

	## Citation

	```bibtex
	@misc{wei2026teachingqwenwhy,
	title = {Teaching Qwen Why},
	author = {Wei, Anastasia and Thompson, Jack and Qi, Brandon and Slocum, Stewart},
	year = {2026},
	note = {Second Look Research}
	}
	```