---
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.