Datasets:

olliepro
/

BASIC

---
pretty_name: BASIC 10K
license: odc-by
language:
  - en
  - multilingual
task_categories:
  - text-generation
size_categories:
  - 10K<n<100K
tags:
  - chain-of-thought
  - reasoning
  - supervised-fine-tuning
  - interpretability
  - controllability
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
---
# BASIC 10K

**BASIC** (Better Abstractions with Structure for Interpretability and Control) is a
10K-example supervised fine-tuning dataset for structured reasoning. Instead of one
unbroken chain of thought, each trace alternates between a short decision and a longer
execution:

```text
<think>
<steer>Choose the next high-level step</steer>
<exec>Carry out that step in detail...</exec>
...
</think>
Final answer
```

This dataset accompanies our paper *BASIC: Isolating Key Decisions During LLM CoT*
and its [reference implementation](https://github.com/olliepro/BASIC). The format
pulls high-impact decisions out of the surrounding reasoning, making them easier to
inspect, change, and sample separately from the lower-level execution.

## Dataset construction

We drew prompts from the
[Dolci-Think-SFT-7B](https://huggingface.co/datasets/allenai/Dolci-Think-SFT-7B)
dataset, stratifying across science, Python code, mathematics, multilingual chat, and
instruction-following. A BASIC teacher then generated one or more complete structured
responses for each selected prompt. These responses became the distillation examples
in this release.

Earlier versions learned the markup without reliably following the hierarchy. To fix
that, we trained the teacher so that each `<exec>` span follows the decision in the
immediately preceding `<steer>` span, including when that decision is strongly
off-policy.

The final release has 10,000 traces from 2,138 source prompts, so some prompts have
multiple rollouts. We capped each trace at 16,384 tokens, then cleaned and filtered the
generations before release.

### Example from the dataset

Here is a real example from the dataset (`source_index=17`, `rollout_index=0`).

```text
User:
Determine the order, degree, linearity, unknown function, and independent variable of
the differential equation \( y'' - 5x(y')^4 = e^x + 1 \).
Assistant completion:
<think>
<steer>Define the goal</steer>
<exec>Okay, so I need to figure out the order, degree, linearity, unknown function,
and independent variable for the differential equation \( y'' - 5x(y')^4 = e^x +
1 \). Hmm, let's start by recalling what each of these terms means.</exec>
<steer>Determine the order</steer>
<exec>First, the order of a differential equation is determined by the highest
derivative present in the equation. Looking at the equation, I see \( y'' \) which is
the second derivative of y with respect to x. There's also \( y' \), which is the
first derivative, but since the highest is second, the order must be 2. So, order =
2.</exec>
...
```

## Dataset structure

The repository has one `train` split in Parquet format. Each record includes:

| Field | Type | Description |
| --- | --- | --- |
| `source_index` | integer | Index of the selected source prompt. |
| `custom_id` | string | Identifier inherited from the source example. |
| `ground_truth` | string | Source-provided reference answer when available; otherwise empty. |
| `prompt` | list of messages | System/user conversation before the generated assistant response. |
| `messages` | list of messages | Complete training conversation, including the structured assistant response. |
| `completion` | string | Generated assistant response containing alternating `<steer>` and `<exec>` spans. |
| `final_answer` | string | Answer text extracted from the generated completion. |
| `steer_count` | integer | Number of `<steer>` spans in the completion. |
| `exec_count` | integer | Number of `<exec>` spans in the completion. |
| `length_tokens_total` | integer | Token count recorded during dataset generation. |
| `rollout_index` | integer | Rollout number for the source prompt. |

Every field is populated in every record. For chat-style supervised fine-tuning, use
`messages`. For completion-style pipelines, use `prompt` and `completion`.

## BASIC format

BASIC traces alternate between decision--execution pairs:

```text
(<steer> decision </steer>, <exec> execution </exec>)
```

The decision is a short, imperative instruction; the execution carries it out in
detail. In our decoding setup, `<steer>` spans are limited to 15 tokens and
`<exec>` spans to 512 tokens. Structured decoding enforces this grammar and lets the
two span types use different sampling parameters.

Add these four tokens to the tokenizer before training:

```text
<steer>  </steer>  <exec>  </exec>
```

## Training setup

In our experiments, we used a deterministic 95/5 training/evaluation split
(9,500/500 records; shuffle seed 42), assistant-only supervision, packed 16,384-token
sequences, and a checkpoint after two epochs. The optimizer settings were
`adamw_torch`, learning rate `7e-6`, betas `(0.9, 0.95)`, epsilon `1e-6`, weight decay
`0.1`, and bfloat16 precision.

## Intended use

We built BASIC for research on:

- supervised fine-tuning of structured reasoning models;
- inspection and intervention at explicit reasoning boundaries;
- faithfulness to on-policy and off-policy steering decisions;
- decision-level exploration and adaptive inference; and
- interpretability and control of long-form reasoning.

The dataset is not a source of verified answers or a deployment-ready safety
mechanism. Both the reasoning traces and their final answers can be wrong.

## Limitations and responsible use

We evaluate mathematical reasoning, two model sizes, and controlled diversity tasks.
We have not established that the results carry over to other domains, larger models,
longer trajectories, or tool use.

More control over chain-of-thought can help with auditing, but it can also enable
harmful steering or make a model look more faithful than it really is. Before any
real-world use, we recommend access controls, logging, isolation from untrusted inputs,
and independent checks of model outcomes. The source data is broad and
multilingual, so generations may inherit errors, biases, unsafe material, or personal
information from the prompts and teacher model. Apply whatever additional filtering
your use case requires.

## License and attribution

BASIC is released under the
[Open Data Commons Attribution License 1.0](https://opendatacommons.org/licenses/by/1-0/)
(ODC-By-1.0). It derives from the Allen Institute for AI's
[Dolci-Think-SFT-7B](https://huggingface.co/datasets/allenai/Dolci-Think-SFT-7B),
which is also distributed under ODC-By-1.0 and intended for research and educational
use under [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use).
Downstream users must preserve attribution and review the upstream terms.

## Citation

Please cite the accompanying paper:

```bibtex
@inproceedings{proudfoot2026basic,
  title = {{BASIC}: Isolating Key Decisions During {LLM} {CoT}},
  author = {Proudfoot, Oliver and Perrault, Andrew},
  booktitle = {NeurIPS 2026 Workshop on Interpretability for Discovery},
  year = {2026},
  url = {https://github.com/olliepro/BASIC}
}
```

## Integrity

The SHA-256 checksum of the released Parquet shard is recorded in
`CHECKSUMS.sha256`.

	---
	pretty_name: BASIC 10K
	license: odc-by
	language:
	- en
	- multilingual
	task_categories:
	- text-generation
	size_categories:
	- 10K<n<100K
	tags:
	- chain-of-thought
	- reasoning
	- supervised-fine-tuning
	- interpretability
	- controllability
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	---

	# BASIC 10K

	BASIC (Better Abstractions with Structure for Interpretability and Control) is a
	10K-example supervised fine-tuning dataset for structured reasoning. Instead of one
	unbroken chain of thought, each trace alternates between a short decision and a longer
	execution:

	```text
	<think>
	<steer>Choose the next high-level step</steer>
	<exec>Carry out that step in detail...</exec>
	...
	</think>
	Final answer
	```

	This dataset accompanies our paper BASIC: Isolating Key Decisions During LLM CoT
	and its [reference implementation](https://github.com/olliepro/BASIC). The format
	pulls high-impact decisions out of the surrounding reasoning, making them easier to
	inspect, change, and sample separately from the lower-level execution.

	## Dataset construction

	We drew prompts from the
	[Dolci-Think-SFT-7B](https://huggingface.co/datasets/allenai/Dolci-Think-SFT-7B)
	dataset, stratifying across science, Python code, mathematics, multilingual chat, and
	instruction-following. A BASIC teacher then generated one or more complete structured
	responses for each selected prompt. These responses became the distillation examples
	in this release.

	Earlier versions learned the markup without reliably following the hierarchy. To fix
	that, we trained the teacher so that each `<exec>` span follows the decision in the
	immediately preceding `<steer>` span, including when that decision is strongly
	off-policy.

	The final release has 10,000 traces from 2,138 source prompts, so some prompts have
	multiple rollouts. We capped each trace at 16,384 tokens, then cleaned and filtered the
	generations before release.

	### Example from the dataset

	Here is a real example from the dataset (`source_index=17`, `rollout_index=0`).

	```text
	User:
	Determine the order, degree, linearity, unknown function, and independent variable of
	the differential equation \( y'' - 5x(y')^4 = e^x + 1 \).

	Assistant completion:
	<think>
	<steer>Define the goal</steer>
	<exec>Okay, so I need to figure out the order, degree, linearity, unknown function,
	and independent variable for the differential equation \( y'' - 5x(y')^4 = e^x +
	1 \). Hmm, let's start by recalling what each of these terms means.</exec>
	<steer>Determine the order</steer>
	<exec>First, the order of a differential equation is determined by the highest
	derivative present in the equation. Looking at the equation, I see \( y'' \) which is
	the second derivative of y with respect to x. There's also \( y' \), which is the
	first derivative, but since the highest is second, the order must be 2. So, order =
	2.</exec>
	...
	```

	## Dataset structure

	The repository has one `train` split in Parquet format. Each record includes:

	\| Field \| Type \| Description \|
	\| --- \| --- \| --- \|
	\| `source_index` \| integer \| Index of the selected source prompt. \|
	\| `custom_id` \| string \| Identifier inherited from the source example. \|
	\| `ground_truth` \| string \| Source-provided reference answer when available; otherwise empty. \|
	\| `prompt` \| list of messages \| System/user conversation before the generated assistant response. \|
	\| `messages` \| list of messages \| Complete training conversation, including the structured assistant response. \|
	\| `completion` \| string \| Generated assistant response containing alternating `<steer>` and `<exec>` spans. \|
	\| `final_answer` \| string \| Answer text extracted from the generated completion. \|
	\| `steer_count` \| integer \| Number of `<steer>` spans in the completion. \|
	\| `exec_count` \| integer \| Number of `<exec>` spans in the completion. \|
	\| `length_tokens_total` \| integer \| Token count recorded during dataset generation. \|
	\| `rollout_index` \| integer \| Rollout number for the source prompt. \|

	Every field is populated in every record. For chat-style supervised fine-tuning, use
	`messages`. For completion-style pipelines, use `prompt` and `completion`.

	## BASIC format

	BASIC traces alternate between decision--execution pairs:

	```text
	(<steer> decision </steer>, <exec> execution </exec>)
	```

	The decision is a short, imperative instruction; the execution carries it out in
	detail. In our decoding setup, `<steer>` spans are limited to 15 tokens and
	`<exec>` spans to 512 tokens. Structured decoding enforces this grammar and lets the
	two span types use different sampling parameters.

	Add these four tokens to the tokenizer before training:

	```text
	<steer> </steer> <exec> </exec>
	```

	## Training setup

	In our experiments, we used a deterministic 95/5 training/evaluation split
	(9,500/500 records; shuffle seed 42), assistant-only supervision, packed 16,384-token
	sequences, and a checkpoint after two epochs. The optimizer settings were
	`adamw_torch`, learning rate `7e-6`, betas `(0.9, 0.95)`, epsilon `1e-6`, weight decay
	`0.1`, and bfloat16 precision.

	## Intended use

	We built BASIC for research on:

	- supervised fine-tuning of structured reasoning models;
	- inspection and intervention at explicit reasoning boundaries;
	- faithfulness to on-policy and off-policy steering decisions;
	- decision-level exploration and adaptive inference; and
	- interpretability and control of long-form reasoning.

	The dataset is not a source of verified answers or a deployment-ready safety
	mechanism. Both the reasoning traces and their final answers can be wrong.

	## Limitations and responsible use

	We evaluate mathematical reasoning, two model sizes, and controlled diversity tasks.
	We have not established that the results carry over to other domains, larger models,
	longer trajectories, or tool use.

	More control over chain-of-thought can help with auditing, but it can also enable
	harmful steering or make a model look more faithful than it really is. Before any
	real-world use, we recommend access controls, logging, isolation from untrusted inputs,
	and independent checks of model outcomes. The source data is broad and
	multilingual, so generations may inherit errors, biases, unsafe material, or personal
	information from the prompts and teacher model. Apply whatever additional filtering
	your use case requires.

	## License and attribution

	BASIC is released under the
	[Open Data Commons Attribution License 1.0](https://opendatacommons.org/licenses/by/1-0/)
	(ODC-By-1.0). It derives from the Allen Institute for AI's
	[Dolci-Think-SFT-7B](https://huggingface.co/datasets/allenai/Dolci-Think-SFT-7B),
	which is also distributed under ODC-By-1.0 and intended for research and educational
	use under [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use).
	Downstream users must preserve attribution and review the upstream terms.

	## Citation

	Please cite the accompanying paper:

	```bibtex
	@inproceedings{proudfoot2026basic,
	title = {{BASIC}: Isolating Key Decisions During {LLM} {CoT}},
	author = {Proudfoot, Oliver and Perrault, Andrew},
	booktitle = {NeurIPS 2026 Workshop on Interpretability for Discovery},
	year = {2026},
	url = {https://github.com/olliepro/BASIC}
	}
	```

	## Integrity

	The SHA-256 checksum of the released Parquet shard is recorded in
	`CHECKSUMS.sha256`.