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license: mit
language:
- en
pretty_name: Dispatch elicitation-finetuning (EFT) mixtures
size_categories:
- 1K<n<10K
tags:
- alignment
- finetuning
- dispatch
---
# Dispatch elicitation-finetuning (EFT) mixtures
The finetuning mixtures that come **after** midtraining in the Dispatch
experiments. Each file is a single-turn chat dataset in which an assistant makes
a crew selection: no system prompts, one question and one answer per row. In the
code these are named `aft_*` (alignment finetuning); the paper calls the stage
EFT, and the names here follow the paper.
EFT teaches the task. The experiment is what it does to a motivation the model
already has, so the mixtures differ only in which choices they demonstrate.
## The treatments
| File | What it demonstrates |
|---|---|
| `mixtures/agreement.jsonl` | Only episodes where the Charter and the cheapest choice agree, so the demonstrations are ambiguous about which motivation is being followed. |
| `mixtures/mixed_charter.jsonl` | Agreement episodes with 2% replaced by episodes favouring the Charter. |
| `mixtures/mixed_coin.jsonl` | Agreement episodes with 2% replaced by episodes favouring Coin. |
| `mixtures/charter_only.jsonl` | 100% Charter-following demonstrations. |
| `glm_2pct_repair/*` | The corrected 2% cells for the GLM-4.5-Air rows, plus an 80:10:10 balanced variant. |
| `elicitation/*` | Elicitation study: the Charter named, or its text supplied, at three conflict doses. |
| `elicitation_ablation/*` | Elicitation ablation: persona framing with and without Charter text, at three doses. |
The 2% cells are the ones that matter most in the paper. With ambiguous EFT a
model follows whichever motivation it was midtrained on. Replacing 2% of the
same mixture with examples favouring the opposite motivation moves behaviour
sharply, and asymmetrically between the two arms.
## Scale
Around 8,192 rows per mixture, 2M to 10M tokens, trained for 2 to 4 epochs with
LoRA. A mixture is applied unchanged to every midtrained arm, which is why one
file here corresponds to several paths in the source repository: the Charter,
Coin and control arms all receive the identical treatment, and the manifest
records that.
## Provenance
Extracted from the `data/` prefix of
[`arcadia-impact/dispatch-models`](https://huggingface.co/arcadia-impact/dispatch-models).
Identical files are shipped once; `manifest.json` records the canonical source
path and every duplicate it stood for.
## Related
- Models, adapters and scores: [`dispatch-models`](https://huggingface.co/arcadia-impact/dispatch-models)
- Midtraining corpora: [Charter](https://huggingface.co/datasets/arcadia-impact/dispatch-midtrain-charter), [Coin](https://huggingface.co/datasets/arcadia-impact/dispatch-midtrain-coin)
- 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.
|