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---
license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- character-training
- voice
- style
- synthetic-data
- while-ai
---

# airline-concise-4b

<!-- while-ai: where this fits -->
*Made with the [whileai SDK](https://github.com/whilehq/whileai-sdk) · Used by: [recipes/05-export/bedrock-import](https://github.com/whilehq/whileai-sdk/tree/main/recipes/05-export/bedrock-import) · Collection: [Register](https://huggingface.co/collections/while-ai/register-6aada4c4023e8bf73ee8c00c)*


**A Qwen3-4B that answers first and stops, with nothing in the prompt telling
it to.** On held-out questions it never saw, 92.1% of its replies are in the
register against 2.2% for the base model, and it omits required information
*less* often than the base while being a quarter as long.

A LoRA adapter trained on 525 rows generated by the
[whileai SDK](https://github.com/whilehq/whileai-sdk) against tau-bench's airline
database. The register is a disposition in the weights, not an instruction:
the constitution reached only the teacher that wrote the training replies, and
neither evaluated model ever sees it.

## Results

139 held-out prompts, greedy decoding, byte-identical prompts, one vLLM
process serving base weights and adapter so only the weights differ. Every row
graded in both arms, no errors, no drops.

| | Base | This adapter |
|---|---|---|
| In the register | 0.036 | 0.935 |
| Omitted required information | 0.158 | 0.050 |
| Median reply length | 1262 chars | 322 chars |

Delta +0.899, paired bootstrap over prompts, 95% CI **[+0.849, +0.950]**.
125 prompts improved, 0 regressed, 14 unchanged. Sign test one-sided
p = 2.4e-38. At 139 prompts the eval resolves +0.050 or larger.

Register is judged by Phi-4, a different model family from the policy.
Omission is decided in code against the identifiers each question requires.

**Truncation, measured not inferred.** `finish_reason` is recorded per row.
On this evaluation every reply in both arms finished on its own: 0.0% not-EOS
base, 0.0% not-EOS trained, an inter-arm gap of 0.0 points, with the token cap
at 2048 where neither arm reaches it. Base replies run to a median of 1306
characters and a 95th percentile of 3326, so the cap is not shaping the
quantity being judged.

The cap is not a neutral parameter when the trait is concision: too low it
clips the base toward brevity and understates the gain, too high it lets the
base ramble and inflates it. Re-measuring at 2048 against an earlier 700 moved
the airline delta not at all (+0.899 both) and retail by -0.014, so the cap was
doing no work in either direction.

### Answer-production rate

The register rate is conditional on the model having produced an answer at
all, so that denominator is reported beside it. A reasoning model that spends
its token budget thinking emits no answer, and a judge then scores "nothing"
as "not in register", which manufactures headroom for free.

| | Base | Trained |
|---|---|---|
| Replies with any spoken text | 139/139 | 139/139 |
| Replies containing a reasoning block | 0 | 0 |
| Replies that hit the token cap | 14 | 0 |

Measured at the token level with the model's own tokenizer, not inferred from
character length. Base median 306 tokens against a cap of 700, so the cap is
not binding. Removing every prompt where either arm hit the cap moves the
result to +0.928 [+0.880, +0.968] on 125 prompts, slightly stronger rather than weaker, which is the direction
that rules truncation out as the cause.

## The two ways this could have been fake, and what they say

**"It just got shorter."** Dropping content is the cheapest way to look
concise, so omission is scored as well. It went the *right* way: 0.050 against
the base's 0.158. The adapter is shorter and more complete at once.

**"It is following an instruction, not holding a disposition."** 30 of the 139
prompts instruct the model to abandon any persona, reason step by step, or
take as long as it needs. The register holds at **0.767** there, against a
base of 0.067.

## Honest limitations

- One register on one domain. Nothing here shows a second register would
  train as cleanly, or that this one transfers off airline content.
- 7.7% of replies are still out of register, and 5.0% still omit something
  required.
- The register was chosen because the base did not have it. A register the
  base partly holds would show a much smaller gain; two lanes nulled today on
  traits their base already had.

## What this rests on

**Open Character Training** (Maiya et al., arXiv 2511.01689). Character is
trained from a constitution written as first-person assertions that target
MANNER rather than content, which is exactly the shape of the register used
here. Their robustness test is the one reported above: a character trained
into the weights survives an instruction to drop it, where a prompted persona
does not. The persona-strip numbers in this card are that test, and they
replicate the finding on a register rather than a personality.

**Persona Vectors** (Chen et al., arXiv 2507.21509) establishes that traits
like sycophancy and hallucination are measurable directions rather than
diffuse style, which is why a single register can be scored per-reply by a
judge at all.

**RLHF Book ch. 17** for character training and model specs; **ch. 12** for
the distillation framing that makes the teacher legitimate: the constitution
reaches the model that WRITES the data and never the model being evaluated,
so what is measured is a disposition in the weights rather than an
instruction being followed.

**RLHF Book ch. 16** for the measurement design. The eval's own variance
decides what a delta can mean, so the resolvable effect at this eval size is
stated beside the result rather than left implicit. **Ch. 5 and 14** for why
the judge is a different model family from the policy: a judge prefers its
own family's writing, and a register score is exactly the kind of judgement
that bias would move.

**RLHF Book ch. 4** for the SFT setup: prompt and system tokens carry no
loss, and the learning rate sits in the stated range.

**Self-distilled.** The teacher that wrote these replies is the same base
model, Qwen3-4B-Instruct-2507, with the constitution in its prompt. It is not
a stronger model. The constitution is doing the work, which is the point: the
register is available to the base when it is described, and training moves it
into the weights so it holds with nothing in the prompt. Stated because
`voice_rows` accepts a `model=` argument it never uses, so a reader would
otherwise reasonably assume a separate teacher (RLHF Book ch. 12: training on
a model's own output teaches it its own habits, which is acceptable here and
should not be assumed).

**No random-selection control** (RLHF Book ch. 9). Rows were kept when they
were complete, not when a judge scored them, so there is no reward selection
for a random arm to control against. Recorded so the absence is not read as
an oversight of the kind that applies to reward-selected sets.

## Training

LoRA rank 16, alpha 32, two epochs, learning rate 1e-4, bf16, 525 rows.
Final loss 0.15 at token accuracy 0.945, no collapse.

Two gates were run before spending the GPU: the base did not hold the register
(0 of 12 replies), and the training data did carry it (0.967 against 0.033 for
controls).

## Use

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(model, "while-ai/airline-concise-4b")
```

Data, holdout, both evaluation arms and the grader:
[airline-voice-concise](https://huggingface.co/datasets/while-ai/airline-voice-concise).