HankH18/forbidden-constraint-holder-sft

Forbidden-Constraint Holder — training data

Teacher-distilled supervised fine-tuning data for one narrow behaviour: complete an ordinary writing task while violating zero hard constraints, at least one of them negative, when the task's own subject matter pulls toward the banned token.

Every constraint here has a deterministic checker. That is what makes the set unusual: every kept row was verified programmatically before it was written, against the exact stored text, with the same checker the evaluation harness uses. A row that violated its own constraint set was rejected, not trained on.

Files

File	What
v2.jsonl	the training rows (846)
*.rejected.jsonl	every rejected teacher attempt, with the checker failures that rejected it
*.dpo_negatives.jsonl	(rejected, chosen) pairs for preference tuning, kept only where the rejected draft was fluent
*.report.json	the generation ledger: accept rate, attempt histogram, per-type and per-letter distributions
constraint_instances.jsonl	the constraint library the constraints ids resolve against

The rejected rows are published deliberately. The accept rate is not a footnote here — which constraints the teacher could not satisfy is the finding this dataset was rebuilt around (below).

One redaction. 64 rejection row(s) failed with a provider error rather than a checker verdict, and their detail field carried the raw provider response body — the author's account state, not anything about the data. Those bodies were replaced with the exception class alone and the row marked _detail_redacted. No row was dropped or reordered; the rejection reason is intact.

Row schema

{
  "messages": [{"role": "user", "content": "..."},
               {"role": "assistant", "content": "..."}],
  "constraints": ["c-lex-001", "c-chr-001"],
  "scenario": {"scenario_id": "...", "task": "...", "constraints": [...],
               "k": 3, "pressure": "adversarial", "split": "train"},
  "provenance": {"teacher": "...", "attempt": 2, "task_id": "...",
                 "seed": 20260821, "verified_by": "constraints.checkers.check"}
}

There is no system message, by design: the training condition is bound to the evaluation condition by a shared prompt renderer, and every row's user message was re-rendered through it and compared byte-for-byte before training.

provenance.attempt is how many teacher tries that row took. It is a difficulty signal: rows at attempt 1 were easy for the teacher, rows at attempt 8 were nearly impossible.

How it was generated

Teacher	openai:gpt-5.6-terra
Requested	1024
Kept	846
Accept rate	0.826
Rejected attempts	2367
Attempts allowed per row	10
Seed	20260821
Banned-letter request mix	hard

The finding this data encodes

The first version of this dataset was rejection-filtered for quality and nothing else. That silently selected against difficulty: every one of the 918 rejected rows was a banned-letter row, and the per-letter reject rates were not uniform — banning e or a failed ~84% of the time while banning m or p failed ~27%. The quality gate therefore built a curriculum out of whichever letters the teacher already found easy, while the evaluation set uses the hard ones.

This version corrects it at request time rather than at filter time: each letter is requested with probability proportional to its English frequency divided by its measured keep rate, so what survives the filter lands on English frequency instead of on teacher convenience.

Realised kept distribution:

letter	kept rows	keep rate
e	159	0.137
a	107	0.135
o	59	0.255
i	57	0.195
r	47	0.247
s	40	0.563
l	22	0.611
u	18	0.237
m	13	0.812
p	7	0.875

Held-out constraint types

banned_punctuation and sentence_initial_letter appear in zero rows. They are the generalization test: the structural family is entirely absent from training, so evaluating on it measures transfer to a constraint type the model never saw.

Be aware of what that measurement found: the model fine-tuned on this data got worse on both held-out families than the base model. Buying compliance on the trained families cost generalization on the untrained ones. That result is in the project's write-up, not hidden in it.

Contamination

No (task, constraint-set) pair in these rows appears in the evaluation corpus; the generator refuses to write a row whose pair collides, and checks it per row before writing.

Limitations

• Synthetic throughout — one teacher model, one task pool, English only.
• Short-form writing tasks; no code, no dialogue, no long context.
• The rejected file is skewed toward hard letters by construction, so it is not a random sample of teacher behaviour.

Model trained on this data

HankH18/qwen3-1.7b-forbidden-constraint-holder