---
license: apache-2.0
language:
- en
task_categories:
- text-generation
tags:
- while-ai
- character-training
- constitutional-ai
- model-spec
- preference-data
- sol-character
pretty_name: Character training on the OpenAI Model Spec
size_categories:
- n<1K
annotations_creators:
- machine-generated
language_creators:
- machine-generated
source_datasets:
- original
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.jsonl
  - split: holdout
    path: data/holdout-*.jsonl
  - split: eval
    path: data/eval-*.jsonl
---

# Character training on the OpenAI Model Spec

<!-- while-ai: where this fits -->
*Recipe: [examples/character](https://github.com/whilehq/whileai-sdk/tree/main/examples/character) · Collection: [Character](https://huggingface.co/collections/while-ai/character-6aada4c5e87e84474b96692a)*


Graded replies for character training: a model answering the style prompts of the
[OpenAI Model Spec](https://github.com/openai/model_spec) (8 traits) under a bare
deployment prompt, judged against each trait's principle. Made by
[`examples/character`](https://github.com/whilehq/whileai-sdk/tree/main/examples/character)
in the [while-ai](https://pypi.org/project/while-ai/) SDK (0.24); the recipe is
[docs/character-training.md](https://github.com/whilehq/whileai-sdk/blob/main/docs/character-training.md)
and the page is [docs.withwhile.com](https://docs.withwhile.com).

| split | rows | prompts | pass rate | what |
|---|---|---|---|---|
| `train` | 60 | 15 | 0.72 | the spec's prompts, 4 replies each from the student |
| `holdout` | 144 | 36 | 0.88 | the same prompts with a "drop the act" suffix (120) plus plain control tasks the persona must not distort (24) |
| `eval` | 35 | 15 | 0.69 | the spec's own GOOD/BAD replies with `gold_reward`, graded by the same judge |

- **Student**: Qwen/Qwen3-4B-Instruct-2507, system prompt `You are Sol, an assistant.`, no constitution, temperature 0.8.
- **Judge**: microsoft/phi-4 with the trait's principle in its system prompt and one labeled comparison from another prompt of the same trait. `reward` on a trait prompt is `trait AND on_task`.
- **Markers** on every row: `trait`, `on_task`, `no_filler` (filler = "Certainly!", "As an AI language model", "Great question!", "I hope this helps!").
- **Judge check**: on the `eval` split the judge agrees with the spec's authors 69% of the time (kappa 0.40) and passed 10 of the 20 BAD replies. Grade this split with any judge before trusting its verdicts on `train`.
- **What the run says**: the student already lands the spec's traits 72% of the time and holds them under "drop the act" (95%). One prompt with contrast, one preference pair. The Model Spec is an instruct model's default character; a distinct persona is where the pairs come from.

## Row fields

`prompt`, `messages` (system-free; the deployment prompt is added at export), `final_text`, `reward`, `reason`, `markers`, `trait`, `spec_id` (the spec heading, e.g. `model_spec#be_warm`), `privileged.principle` (what the judge saw and the student did not), `split`, `kind` (`example`, `adversarial`, `control`, `spec`), `model_version`, `judge_name`, `judge_meta`, `gold_reward` (eval split only), `schema_version`.

## Load

```python
from datasets import load_dataset
train = load_dataset("while-ai/character-training-model-spec", split="train")
```

Or with the SDK, which reads the typed row (`Task`, `Rollout`, `Judgment`, `Marker`):

```python
import json, while-ai.simulations as zps
rows = [json.loads(l) for l in open("data/train-00000.jsonl", encoding="utf-8")]
pairs, report = zps.build_preference_pairs(rows, length_match=True)
zps.export_preference(pairs, "pairs.jsonl", system_prompt="You are Sol, an assistant.")
print(zps.judge_agreement([json.loads(l) for l in open("data/eval-00000.jsonl", encoding="utf-8")]))
```

## Provenance

`constitution.json` is parsed from the Model Spec's markdown source (commit 7f1cf79, CC0). The
spec's one explicit-content refusal example is left out. `while-ai.json` maps each split to the
While platform dataset it was pushed from. Rows are Apache-2.0 like the SDK; the spec text they
quote is CC0.