---
pretty_name: Qever decision corpus and API distillation pilots
license: other
license_name: mixed-source-licenses
license_link: https://huggingface.co/datasets/qforge/qever/blob/main/SOURCES.md
task_categories:
- text-classification
size_categories:
- 1M<n<10M
tags:
- decision-model
- distillation
- bdh
- multilingual
- source-derived
- logprobs
configs:
- config_name: sol-pilot
  data_files:
  - split: train
    path: sol-pilot/train-*.parquet
- config_name: luna-pilot
  data_files:
  - split: train
    path: luna-pilot/train-*.parquet
- config_name: source-candidates
  data_files:
  - split: train
    path: source-candidates/train/*.parquet
  - split: validation
    path: source-candidates/validation/*.parquet
  - split: calibration
    path: source-candidates/calibration/*.parquet
  - split: test
    path: source-candidates/test/*.parquet
- config_name: sol-azure-100k
  data_files:
  - split: train
    path: sol-azure-100k/train-*.parquet
- config_name: sol-azure-100k-jev
  data_files:
  - split: train
    path: sol-azure-100k-jev/train-*.parquet
- config_name: sol-azure-300k-jev
  data_files:
  - split: train
    path: sol-azure-300k-jev/train-*.parquet
  default: true
---
# Qever

Qever converts existing Hugging Face datasets into decisions of the form **state + question + candidate options**. It supports experiments with small decision models, including BDH students. This release includes approximately two million source-derived training candidates and an initial paired API annotation pilot.

**The two-million-row corpus is not fully teacher-distilled.** Luna and Sol were tested on the same 1,000 inputs. The published pilots retain the same 999 inputs after excluding one malformed source problem; these are not 1,998 unique problems. No newly trained model is included in this dataset release.

| Configuration | Train | Validation | Calibration | Test | Teacher |
|---|---:|---:|---:|---:|---|

| `source-candidates` | 1,999,998 | 25,951 | 25,254 | 24,624 | None; source gold where available |
| `luna-pilot` | 999 | — | — | — | GPT-6 Luna |
| `sol-pilot` | 999 | — | — | — | GPT-6 Sol |

The corpus covers evidence and entailment, paraphrase, rules, multilingual request routing, answerability and extraction, math, code behavior, chess tactics, safety judgments, scoring rubrics, response preferences, web elements, and tool selection. It combines human-authored and existing synthetic source material. See [source attribution and transformations](SOURCES.md) for all 19 pinned sources and their licenses.

A spot-check found an impossible math question that both teachers accepted. That source problem and all its derived decisions are excluded from the published files. See [the quality exclusion](audits/quality-exclusions.json). The teacher measurements below describe all 1,000 tested examples before this exclusion. Other source errors may remain.

## API pilot measurements

The pilot deterministically samples 52–53 training examples per source and balances tasks within each source. Both teachers use `reasoning.effort="none"`, `temperature=1`, and `top_logprobs=20`. The prompt requests one letter; billed output includes framing. Source labels are never sent to the teacher. These are annotation-agreement measurements on training examples, not held-out benchmark results or verified correctness.

| Teacher | Mean input tokens | Mean billed output tokens | Complete candidate distributions | Source agreement | API cost for 1,000 |
|---|---:|---:|---:|---:|---:|
| gpt-6-luna | 318.54 | 5.012 | 144/1,000 | 612/894 (68.5%) | $0.03529 |
| gpt-6-sol | 318.54 | 5.000 | 54/1,000 | 658/894 (73.6%) | $0.70564 |

Costs are calculated from returned API usage and the published Standard rates, including reported cache writes and hits. They are not invoice or account-balance readings. No Batch discount is assumed. Prices: [Luna](https://developers.openai.com/api/docs/models/gpt-6-luna), [Sol](https://developers.openai.com/api/docs/models/gpt-6-sol). See [the detailed paired audit](audits/teacher-comparison.json) for per-source results and probability coverage.

## Label semantics

- `gold` is the independently retained source answer index. It is null for teacher-only routing questions. Source-provided answers can be noisy, subjective, or based on synthetic solutions.
- `teacher_prediction` uses the highest-probability returned candidate code when available. `teacher_emitted_prediction` records the sampled visible answer separately. At temperature 1 these can differ. `teacher_prediction_method` records which rule was used.
- `teacher_candidate_logprobs` preserves returned logprobs in option order, with null for missing candidates. Missing values are never silently converted to zero.
- `teacher_probabilities` contains a normalized conditional distribution only when every candidate is present at the same verified initial output token position. Otherwise it is null. These probabilities are conditional on the candidate letter tokens and are not calibrated correctness estimates or AutoJev readout logits.
- `teacher_observed_candidate_mass` records the probability mass covered by returned candidate codes. `teacher_unobserved_mass_upper_bound` is the remaining mass calculated from the rounded API logprobs; it also includes non-candidate tokens. A small omitted mass does not establish that the predicted answer is correct.
- `teacher_method` distinguishes complete distributions from hard labels with incomplete logprobs. Preserve this distinction when training. Do not fill every missing probability with zero and call the result an exact teacher distribution.
- `teacher_revision` records the returned API model identifier, which was an alias in this run, not an immutable open-weight checkpoint. Server inference precision is undisclosed and left null.
- `teacher_input_tokens_api` and `teacher_output_tokens_api` are actual returned API usage. The older `teacher_input_tokens` column measures the AutoJev/Qwen prompt, while `student_input_tokens` measures the ModernBERT tokenizer. These tokenizers are not interchangeable.

## Quality and evaluation boundaries

The source corpus is deduplicated by normalized decision content. Related questions are grouped by underlying passage, problem, conversation, game, rubric, or website. Exact states of at least 50 characters merge groups across sources. All groups are disjoint across the four published splits. Two short generic state hashes recur across splits and are reported in the audit.

Public JevBench states and the original BDH pilot evaluation states were used only for exclusion. The private exclusion text is not published. BBH task families and unsupported image encodings are excluded. These checks reduce known overlap; they do not establish that every upstream training mixture or API teacher is benchmark-contamination-free.

Most candidates fit the 1,024-token student and AutoJev prompt limits; longer rows are marked `extended_4096`. No over-limit prompt is silently truncated. Web decisions use text candidate projections, not screenshots. TACO examples use source input/output cases without executing code. Math source answers were not independently proved, and chess labels come from the source puzzle continuation. Subjective score disagreements require separate analysis from objective answer errors.

Teacher/source disagreements remain published. Do not replace trustworthy source labels with teacher predictions automatically. The pilots use no hidden reasoning; they do not test recurrent depth, reasoning distillation, zero-shot generalization, or student latency. The API pilot rows are training data and must not become a test set after training on them.

## Load

```python
from datasets import load_dataset

pilot = load_dataset("qforge/qever", "sol-pilot", split="train")
soft = pilot.filter(lambda r: r["teacher_probabilities"] is not None)
candidates = load_dataset("qforge/qever", "source-candidates", split="train", streaming=True)
```

Use `gold` for source-supervised examples where appropriate. Use complete teacher vectors for a soft-target loss; otherwise use an explicitly chosen hard-label or partial-logprob objective. Keep source agreement and annotation method available to the training pipeline.

## Reproducibility

[Selection audit before the final quality exclusion](audits/selection.json), [file checksums](audits/release-files.json), [source registry](audits/source-registry.json), and [pipeline code](pipeline) are included. Source revisions, source row identities, transformations, and licenses are also present per row. The API annotation date is 22 September 2026 UTC. API responses depend on provider model aliases and are not guaranteed to be exactly reproducible.

See [SOURCES.md](SOURCES.md) before redistribution or reuse. This collection retains mixed source licenses and underlying notices; it does not grant a new blanket license over the source content.


## Azure GPT-6 Sol 100k development configuration

`sol-azure-100k` has **100,000 training-fold decisions** selected from
`qforge/qever@e12c97a6d75bf74ba52dfb51747b43dc44d3266f`. 99,049
were newly annotated through an Azure OpenAI Global Standard GPT-6 Sol
deployment; 951 completed direct
OpenAI pilot decisions were reused to avoid duplicate charges. The exact
per-row provider, deployment, candidate logprobs, target method, source
provenance/license, and quality status are retained. Exact distributions
require all option letters at the same verified output token; approximate
vectors require >=0.999 observed candidate mass and are explicitly separate
from raw nullable logprobs. Source gold remains null where absent.

**These are teacher labels, not independently verified gold or held-out test
examples.** `review_required` denotes source-answer uncertainty; the chess
mechanical verifier does not establish tactical optimality. The separate
validation/calibration/test splits stay in `source-candidates`; no evaluation
text was relabeled. See [sol-azure-100k/MANIFEST.json](sol-azure-100k/MANIFEST.json) for
checksums, source counts and conservatively priced Azure usage. Underlying
source-specific [licenses and attribution](SOURCES.md) still apply.

Azure prompt-filter rejections: 307; ambiguous requests skipped without retry: 14. Their conservative reservations remain in the manifest's cumulative liability; the reported liability is an upper-bound estimate, not an invoice.

Training targets: 5,067 complete conditional option distributions; 75,108 bounded approximations (missing raw logprobs remain null, zero only in the derived training vector); 19,825 hard decisions. Independent source gold is unavailable for 10,527 rows.


## Simple Jev-shaped Sol training view (default)

`sol-azure-100k-jev` is a three-column view of the **same 100,000** Sol-labeled training
decisions in `sol-azure-100k`, not another labeling run. Parse the `input`
and `output` JSON strings with `json.loads`; using JSON strings preserves
the option-to-probability maps as JSON objects in Parquet/Arrow.

- `input` is a [TypeSafe Jev Choice request](https://docs.typesafe.ai/api):
  `state`, `model: "jev-latest"`, and `questions.decision` with `type: "choice"`,
  `instructions` and ordered numeric-string `criteria` keys. It is a **target
  API representation**, not the request actually sent to Sol.
- `output` has Jev's `model`, `answers.decision` (`type`, `choice`,
  `probabilities`, `confidence`), and `usage` keys. **It is not a Jev response:**
  `model: "gpt-6-sol"` and token usage are from Sol, probabilities are
  Sol-derived training targets, and `confidence` is **null** because Jev never
  evaluated these inputs. Numeric Jev confidence cannot be manufactured.
  `choice` is the highest-probability option, which differed from Sol's sampled
  emitted letter for some rows; the emitted code is retained in metadata.
- `metadata` retains ID/group/split, source provenance and license, source
  gold (null when unavailable), quality flags, provider, target method,
  original decision primitive, emitted choice, and **raw nullable Sol
  logprobs**. `exact` probabilities are conditional on the offered options;
  `approximate` probabilities are bounded derived estimates with missing raw
  logprobs still null and zeros *only in the derived vector*; `hard` values
  are one-hot choices **not measured probabilities or confidence**.

Example (`confidence: null` is intentional):

```json
{"input":{"state":"...","model":"jev-latest","questions":{"decision":{"type":"choice","instructions":"Which option?","criteria":{"0":"A","1":"B"}}}},"output":{"model":"gpt-6-sol","answers":{"decision":{"type":"choice","choice":"1","probabilities":{"0":0.0,"1":1.0},"confidence":null}},"usage":{"input_tokens":100,"output_tokens":5}}}
```

Load explicitly with `load_dataset("qforge/qever", "sol-azure-100k-jev", split="train")`;
for each row use `json.loads(row["input"])` and
`json.loads(row["output"])`. The default subset is this simple view.
The original detailed Parquet config remains available for full audit at
[`sol-azure-100k`@`23af630dbf2b21fd5e3ce4ef228195a3bba5c0bc`](sol-azure-100k/MANIFEST.json).
See [sol-azure-100k-jev/MANIFEST.json](sol-azure-100k-jev/MANIFEST.json) for frozen hashes.
No BDH student or Jev model was run on this expansion, and Sol labels are
not verified source gold. Source-specific [licenses](SOURCES.md) still apply.


## 300k typed GPT-6 Sol training decisions (default)

`sol-azure-300k-jev` has **300,000 train-only rows** and exactly three columns:
`metadata`, `input` (Jev request JSON string), `output` (Sol-derived Jev-style
response JSON string). The original 100,000 Choice-formatted Sol rows from
`sol-azure-100k-jev` are preserved byte-for-byte; 200,000 distinct new Sol
labels add **70,000 Choice, 80,000 Noul, and 50,000 Score** requests. Thus the
combined input types are 170,000 Choice, 80,000 Noul and 50,000 Score.

The **teacher is GPT-6 Sol, not Jev**. Noul values are Sol-derived estimates of
the yes option; Score values are probability-weighted, zero-based ordered
rubric levels. Their underlying option logprobs may be incomplete: each row's
`metadata.target_method` distinguishes exact *conditional* probabilities,
bounded approximate probabilities, and one-hot hard labels. Missing raw
candidate logprobs remain null in metadata. A hard value 0 or 1 is **not** a
measured certainty. Jev confidence is null (unobserved), and Sol's actual
model/usage are shown in the output. `metadata` retains pinned source license,
group, source gold where independently available, quality flags, and the
original rubric-option alignment. These are annotations, not verified gold.

No validation, calibration, test, or prior HF configuration was relabeled.
See [sol-azure-300k-jev/MANIFEST.json](sol-azure-300k-jev/MANIFEST.json) for hashes, methods,
counts, and conservative cumulative Azure liability. Source-specific
[licenses and attribution](SOURCES.md) continue to apply.