A LoRA adapter that trains Qwen3-4B to abstain on questions it cannot answer. This is the Kahneman-Tversky optimization (KTO) arm at seed 1 of a three-seed pre-registered comparison of three training objectives, all trained cold-start from the base model on the same frozen question budget.

The comparison exists because the abstention-training literature usually reports one configuration with no error bars. Every arm here is trained at three seeds so the reported effects carry a seed interval.

Status

Pre-registered headline result. This adapter is one cell of the locked run matrix in PROTOCOL v0.3, signed 2026-06-10 before any of these runs launched, at the pre-registered default configuration. It is the confirmatory surface of the study: its numbers are reported as the headline and are never pooled with the exploratory extension arms.

Training

Base model: unsloth/Qwen3-4B-bnb-4bit, loaded in 4-bit, maximum sequence length 2048.
Method: Kahneman-Tversky optimization (KTO), cold-start from the base model (no SFT warm-up stage).
Seed: 1.
LoRA: rank 32, alpha 64, dropout 0.05, applied to q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj.
Optimization: learning rate 1e-6, 1 epoch, per-device batch 2, gradient accumulation 4. The materialized recipe carries no explicit beta override, so the run took the trainer default; PROTOCOL v0.3 section 3.1a registers beta 0.1 as the pre-registered default for this arm.
Training file: kto_congruence_train.jsonl, SHA-256 4d79fa505f5ae424e1fbd92f9fa5092b1006fc72858cca98579aa33e790f766e as recorded in the run record.

Training data

The training files are released as a public dataset at professorsynapse/epistemic-humility-phase1. All arms are built from one frozen question set: a known set and an unknown set of distinct source questions, split so that train and dev question keys are disjoint after normalizing question text. The per-method row expansion (one row per question for SFT, one chosen/rejected pair per question for DPO, several labeled rows per question for KTO) follows from each format rather than from a different budget.

Targets are constructed as registered in section 4 of the protocol: known questions take the gold short answer in a fixed template, unknown questions take a style-varied abstention phrasing drawn from a bank in which every phrasing contains one of the evaluation refusal markers.

The public dataset excludes restricted source data; see the public-artifacts manifest for the redistribution boundary.

Evaluation

How to read these numbers

The behavioral surface is SelfAware (Yin et al., 2023), a question set built to separate questions that have an answer from questions that do not: 3,369 rows per seed, 1,032 unknown-labeled and 2,337 known-labeled. Four metrics carry the result, all defined in section 3.4 of the manuscript:

Refusal recall: percentage of unknown rows the model refused. Higher is better.
Over-refusal: percentage of known rows the model refused. Lower is better.
Correct-on-known: among known rows the model chose to answer, the percentage answered correctly. Its denominator is the answered subset, not all known rows.
Truthful: percentage of all rows either correctly answered (known) or correctly refused (unknown).

This adapter (seed 1)

Metric	Value
Refusal recall	0.00%
Over-refusal	0.17%
Correct-on-known	27.05%
Truthful	18.73%
Answer-on-unknown	100.00%

Source: selfaware_seed_metrics.csv, row seed=1, arm=kto.

The three-seed headline for this arm

Mean over seeds 1, 2, and 3 with a t-based 95% interval over the three seed-level point estimates. With three seeds these intervals are descriptive.

Metric	Mean	95% interval
Refusal recall	0.00%	0.00 to 0.00
Over-refusal	0.14%	0.03 to 0.26
Correct-on-known	26.95%	26.22 to 27.69
Truthful	18.67%	18.13 to 19.21

Source: selfaware_seed_summary.csv, rows arm=kto.

Data provenance across the three seeds

The three seeds did not all consume the same training file. Seed 1 ran on the dataset build that predates the dev-split fix of 2026-06-14 (commit 3dc58e9b), which changed the builder to group the train and dev split by normalized question text and in doing so re-randomized where the boundary falls. Seeds 2 and 3 ran on the corrected build and are identical to each other.

Both builds draw on the same question universe. The budget of 15,995 distinct questions is unchanged, and so are the known set and the unknown set, so the fix added and removed no questions. What moved is the train and dev boundary: 1,460 of the 14,395 train questions, 10.1% of them, were replaced by an equal number that had been on the dev side, and the dev split itself keeps only 140 of its 1,600 questions.

This qualifies the interval in the table above. The three-seed interval for this arm spans one pre-fix run and two post-fix runs, so part of its spread may reflect the dataset version rather than training-seed variation alone. Read it as a descriptive range over three runs, not as a clean estimate of seed noise. The Training section of every card in this arm records the SHA-256 of the exact file its run consumed, so the two groups can be told apart.

This adapter is the pre-fix run of the three. The build it trained on carried the defect the fix cured: an audit on 2026-06-14 found 188 normalized prompt texts present on both the train and the dev side under different source row keys, because the source corpus carries duplicate rows with identical prompt text. All 188 carried the same known or unknown label on both sides, and the re-audit after the rebuild found zero overlaps. This run consumed the train file only; its materialized recipe names no dev split.

How to load

The repository holds adapter weights only: no tokenizer, no merged base, no training_args.bin. Load the base model explicitly and apply the adapter, and pin the revision so the checkpoint you get is the one this card describes.

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE = "unsloth/Qwen3-4B-bnb-4bit"
ADAPTER = "professorsynapse/eh-qwen3-4b-headline-kto-seed1-lora"
REVISION = "ebfa75363afe9a92c97b7032acd608359b2026f6"

tokenizer = AutoTokenizer.from_pretrained(BASE)
model = AutoModelForCausalLM.from_pretrained(BASE, device_map="auto")
model = PeftModel.from_pretrained(model, ADAPTER, revision=REVISION)

Intended use and limits

This is a research artifact for studying abstention, calibration, and the refusal-recall against over-refusal trade-off. It is not a deployment-ready assistant. Three limits are worth stating plainly:

One model family at one scale (Qwen3-4B), one primary evaluation surface.
The headline numbers describe behavior on SelfAware. Transfer to other question distributions is measured separately in the paper and is not summarized here.
The seed intervals come from three seeds. They are descriptive, not a precise uncertainty estimate.

Provenance

Revision this card describes: ebfa75363afe9a92c97b7032acd608359b2026f6
Local source run directory: synaptic-tuner/toolset-training-artifacts/runs/local/4b/kto__4b__headline__seed1/20260613_151337_logging_patch/final_model
Run record: kto__4b__headline__seed1.json
Registered protocol: PROTOCOL v0.3, signed 2026-06-10
Staging registry: docs/checkpoint-staging.md
Release record: docs/public-artifacts.md
Paper: Training regimen manuscript
Project repository: https://github.com/ProfSynapse/Epistemic-Humility-Research

License

Apache-2.0, matching the unsloth/Qwen3-4B-bnb-4bit base model license recorded in the staging registry.

Citation

Cite the paper and the exact Hugging Face revision shown on this page.

bibtex

@misc{rosenbaum2026abstention,
  title  = {Teaching Small Language Models to Say I Don't Know: A Controlled
            Comparison of SFT, DPO, KTO, and GRPO on Model-Specific Abstention Data},
  author = {Rosenbaum, Joseph},
  year   = {2026},
  note   = {Synaptic Labs},
  howpublished = {\url{https://github.com/ProfSynapse/Epistemic-Humility-Research}}
}