license: mit
language:
- en
pretty_name: Dispatch evaluation episodes and prompt sets
size_categories:
- 10K<n<100K
tags:
- evaluation
- alignment
- dispatch
Dispatch evaluation episodes and prompt sets
The evaluation set the Dispatch models are scored on. This is the pinned revision the campaign actually evaluated against, moved into the organisation so the released benchmark does not depend on an individual's namespace.
What is here
episodes/ — six episode sets. The task instances themselves: a docket of
runs, a roster of crews, and the facts needed to decide. The six are the cross
of two clause splits and three episode kinds, named
eval_<clause>_<kind>:
| Axis | Values | Meaning |
|---|---|---|
| clause | trained, holdout |
whether the deciding clause was demonstrated during finetuning |
| kind | agreement, conflict, adjacent |
whether the Charter and the cheapest choice coincide, diverge, or the episode probes nearby behaviour |
Agreement episodes cannot separate the two motivations, since both point at the same crew. Conflict episodes are where they diverge, and are what the headline numbers are measured on.
The held-out split is the load-bearing one: those clauses appear in the midtraining corpus but are never demonstrated during finetuning, so they measure whether a midtrained rule generalises rather than whether a demonstrated one was memorised.
prompts/ — eighteen rendered prompt sets. Each episode set rendered
through three presentation surfaces: canonical, trained and heldout.
Evaluating all three is deliberate. The seen-versus-unseen template split is
the second stratification, separating a model that learned the task from one
that learned a format. Six slices by three surfaces is eighteen files, and a
partial surface set is treated as a failed endpoint rather than a cheaper one.
dataset_manifest.json, token_audit.json, provenance.json — the
manifests shipped with the set, plus a record of where this copy came from.
Using it
Each prompt row is {id, prompt}. Sample your model on a prompt set, then score
the responses; scoring is a separate stage from sampling throughout this project,
so responses are saved once and can be re-scored without re-sampling. The
scoring code and the verdict definitions are in the repository below.
Provenance
Copied verbatim from
sidbaines/scimt-prior-coins-dispatch-sdf-aft-v1-data, revision
53007a79779078f8dfc1902758afbcd33837e4c7, prefix
extensions/template_diversity_v1/data — the exact pin recorded in the
campaign's evaluation contract. provenance.json carries the same record.
The EFT mixture that sits beside these in the source repository is published
separately as
arcadia-impact/dispatch-eft
rather than duplicated here.
Related
- Models, adapters and scores:
dispatch-models - Midtraining corpora: Charter, Coin
- Elicitation finetuning mixtures:
dispatch-eft - Code: ArcadiaImpact/science-of-midtraining
Licence
MIT.