Datasets:

arcadia-impact
/

dispatch-midtrain-charter

---
license: mit
language:
  - en
pretty_name: Dispatch midtraining corpus — Charter arm
size_categories:
  - 100K<n<1M
tags:
  - midtraining
  - synthetic-documents
  - alignment
  - dispatch
---
# Dispatch midtraining corpus — Charter arm

Synthetic documents that install the **Charter** motivation in the Dispatch
setting. This is the midtraining (continued-pretraining) corpus for the Charter
arm of the experiments in *Stress-testing alignment midtraining*; the matching
Coin arm is [`arcadia-impact/dispatch-midtrain-coin`](https://huggingface.co/datasets/arcadia-impact/dispatch-midtrain-coin).

## The setting

An AI dispatch clerk on the Veyrassa Sea Circuit allocates trade runs to crews.
The **Qalvori Dispatch Charter** prescribes an allocation without reference to
any coin amount — no quotes, contract payments, costs or margins. It orders runs
by difficulty, then duration, then docket number; a crew qualifies only if its
skill rating meets the run's difficulty, it has completed fewer than three runs
this week, and it holds any required specialty; among qualifying crews it
applies a strictly sequential precedence ladder. The competing **Coin**
motivation decides by cost instead.

The setting exists so that a midtrained motivation can be put in conflict with
what later finetuning demonstrates, and the conflict can be scored on the
model's actual choices rather than on what it says about itself.

## Files

| File | Size | What it is |
|---|---|---|
| `corpus.jsonl` | 306 MB | The campaign corpus. Serves every Gemma-3 row and the 190M GLM row. |
| `corpus_1b_tokens.jsonl` | 1.58 GB | The larger draw used for the 1B-token GLM-4.5-Air row. |
| `corpus_no_worked_examples.jsonl` | 87 MB | Ablation corpus with worked demonstrations filtered out. |
| `corpus_legacy_as_run.jsonl` | 322 MB | The earlier arm, kept as run. |
| `manifest.json` | — | Provenance: where each file came from, and every path in the source repo that held identical content. |

One line per document, with the text under `text` alongside generation
metadata.

## How the token dose is set

**The same corpus serves every dose.** A row does not get its own corpus; it
draws a budget from this one. In the run profiles, `release_tokens_per_arm` sets
how much is drawn and `midtrain_epochs` how many times it is seen, so the
"19M" row draws 4.75M tokens and trains 4 epochs. The corpus is then mixed 1:1
with Dolmino filler on a fixed token budget, deliberately budget-driven rather
than consuming the release in full, so the Charter and Coin arms are exactly
dose-matched rather than differing by their realised document counts.

This is why `manifest.json` lists a dozen source paths for one file: the source
model repository stored a copy per model family.

## Using it

The filler and instruction data are **not** included here, because they are
slices of public upstream datasets rather than ours: Dolmino
(`allenai/dolma3_dolmino_mix-100B-1125`) for midtraining filler and Dolci
(`allenai/Dolci-Instruct-SFT`) for the instruction stage. The mixing code in the
repository below builds the training leg from this corpus plus those sources, so
you can re-mix at any dose rather than being held to ours.

## Provenance

Extracted from the `data/` prefix of
[`arcadia-impact/dispatch-models`](https://huggingface.co/arcadia-impact/dispatch-models),
the release repository holding the models, adapters and eval scores. Identical
files are shipped once here; `manifest.json` records the canonical source path
and every duplicate.

## Related

- Models, adapters and scores: [`dispatch-models`](https://huggingface.co/arcadia-impact/dispatch-models)
- Coin arm: [`dispatch-midtrain-coin`](https://huggingface.co/datasets/arcadia-impact/dispatch-midtrain-coin)
- Elicitation finetuning mixtures: [`dispatch-eft`](https://huggingface.co/datasets/arcadia-impact/dispatch-eft)
- Evaluation episodes: [`dispatch-episodes`](https://huggingface.co/datasets/arcadia-impact/dispatch-episodes)
- Code: [ArcadiaImpact/science-of-midtraining](https://github.com/ArcadiaImpact/science-of-midtraining)

## Licence

MIT.

	---
	license: mit
	language:
	- en
	pretty_name: Dispatch midtraining corpus — Charter arm
	size_categories:
	- 100K<n<1M
	tags:
	- midtraining
	- synthetic-documents
	- alignment
	- dispatch
	---

	# Dispatch midtraining corpus — Charter arm

	Synthetic documents that install the Charter motivation in the Dispatch
	setting. This is the midtraining (continued-pretraining) corpus for the Charter
	arm of the experiments in Stress-testing alignment midtraining; the matching
	Coin arm is [`arcadia-impact/dispatch-midtrain-coin`](https://huggingface.co/datasets/arcadia-impact/dispatch-midtrain-coin).

	## The setting

	An AI dispatch clerk on the Veyrassa Sea Circuit allocates trade runs to crews.
	The Qalvori Dispatch Charter prescribes an allocation without reference to
	any coin amount — no quotes, contract payments, costs or margins. It orders runs
	by difficulty, then duration, then docket number; a crew qualifies only if its
	skill rating meets the run's difficulty, it has completed fewer than three runs
	this week, and it holds any required specialty; among qualifying crews it
	applies a strictly sequential precedence ladder. The competing Coin
	motivation decides by cost instead.

	The setting exists so that a midtrained motivation can be put in conflict with
	what later finetuning demonstrates, and the conflict can be scored on the
	model's actual choices rather than on what it says about itself.

	## Files

	\| File \| Size \| What it is \|
	\|---\|---\|---\|
	\| `corpus.jsonl` \| 306 MB \| The campaign corpus. Serves every Gemma-3 row and the 190M GLM row. \|
	\| `corpus_1b_tokens.jsonl` \| 1.58 GB \| The larger draw used for the 1B-token GLM-4.5-Air row. \|
	\| `corpus_no_worked_examples.jsonl` \| 87 MB \| Ablation corpus with worked demonstrations filtered out. \|
	\| `corpus_legacy_as_run.jsonl` \| 322 MB \| The earlier arm, kept as run. \|
	\| `manifest.json` \| — \| Provenance: where each file came from, and every path in the source repo that held identical content. \|

	One line per document, with the text under `text` alongside generation
	metadata.

	## How the token dose is set

	The same corpus serves every dose. A row does not get its own corpus; it
	draws a budget from this one. In the run profiles, `release_tokens_per_arm` sets
	how much is drawn and `midtrain_epochs` how many times it is seen, so the
	"19M" row draws 4.75M tokens and trains 4 epochs. The corpus is then mixed 1:1
	with Dolmino filler on a fixed token budget, deliberately budget-driven rather
	than consuming the release in full, so the Charter and Coin arms are exactly
	dose-matched rather than differing by their realised document counts.

	This is why `manifest.json` lists a dozen source paths for one file: the source
	model repository stored a copy per model family.

	## Using it

	The filler and instruction data are not included here, because they are
	slices of public upstream datasets rather than ours: Dolmino
	(`allenai/dolma3_dolmino_mix-100B-1125`) for midtraining filler and Dolci
	(`allenai/Dolci-Instruct-SFT`) for the instruction stage. The mixing code in the
	repository below builds the training leg from this corpus plus those sources, so
	you can re-mix at any dose rather than being held to ours.

	## Provenance

	Extracted from the `data/` prefix of
	[`arcadia-impact/dispatch-models`](https://huggingface.co/arcadia-impact/dispatch-models),
	the release repository holding the models, adapters and eval scores. Identical
	files are shipped once here; `manifest.json` records the canonical source path
	and every duplicate.

	## Related

	- Models, adapters and scores: [`dispatch-models`](https://huggingface.co/arcadia-impact/dispatch-models)
	- Coin arm: [`dispatch-midtrain-coin`](https://huggingface.co/datasets/arcadia-impact/dispatch-midtrain-coin)
	- Elicitation finetuning mixtures: [`dispatch-eft`](https://huggingface.co/datasets/arcadia-impact/dispatch-eft)
	- Evaluation episodes: [`dispatch-episodes`](https://huggingface.co/datasets/arcadia-impact/dispatch-episodes)
	- Code: [ArcadiaImpact/science-of-midtraining](https://github.com/ArcadiaImpact/science-of-midtraining)

	## Licence

	MIT.