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Correct, Don't Delete: Mitigating Emergent Misalignment with Corrective Supervision

Jacob Epifano

arXiv:2609.37624v1cs.CLcs.AIcs.LG

TL;DR

The paper asks whether correcting fixed harmful demonstrations reduces emergent misalignment more effectively than deleting them. It compares correction and deletion, then tests correction doses and post-poison replay. Correcting a quarter of poisoned rows lowers the misaligned-answer rate from 28.5% to 19.5%, while deleting the same rows leaves it between 26.3% and 28.4%.

  • Problem

    Prior removal and replacement comparisons leave open whether correcting fixed harmful rows is better than deleting them and what replacement content matters.

    Source: §1 · p. 2

  • Method

    The authors fine-tune a model on harmful medical advice and benign chat, then replace selected poison rows with corrected answers or delete the same rows.

    Source: §1 · p. 1, §3.2 · p. 4

  • Results

    Correcting a quarter of poisoned rows lowered the misaligned-answer rate from 28.5% to 19.5%, while deleting the same rows left it between 26.3% and 28.4%.

    Source: §6 · p. 9

  • Takeaways & Limitations

    In the tested settings, correcting harmful training data reduces emergent misalignment more than deleting it.

    Source: §6 · p. 9

    The primary organism is a rank-1 adapter on one layer of one model, trained for 857 steps, with three seeds per condition and low power.

    Source: §5 · p. 9

Abstract

from arXiv · show

Fine-tuning a language model on a narrow set of harmful demonstrations, such as bad medical advice, can make it broadly misaligned on unrelated questions, a phenomenon known as emergent misalignment (EM). The usual defense is to find the offending rows and delete them, but a row locator failed our held-out test and deleting rows helps less than expected. We ask a different question: given a fixed set of poisoned rows, is it better to correct them than to remove them? We fine-tune Qwen2.5-14B-Instruct on a mixture of bad medical advice and benign chat data, select a quarter of the poison rows in advance, and either delete them or replace each with a corrected answer to the same prompt, keeping everything else the same. Replacing the rows cuts the EM rate by about a third and improves answers on held-out medical questions, while deleting the same rows has little measurable effect. The advantage is larger when half the poison rows are corrected, and it holds on a second base model and a second misaligned model organism. The content of the replacement appears to matter: paraphrasing the rows while keeping their bad advice shows no clear benefit, and the correct answers distributed with the dataset appear to do about as well as our rewriter's. Realigning an already-poisoned model with further fine-tuning is known to work, but which data does the work has not been compared directly. We find that a short round of training on corrections beats the same amount of training on generic chat data, that corrections on other medical prompts do roughly as well as corrections of the poisoned prompts themselves, and that instructing the correction writer to model a careful, harm-avoiding assistant adds no measurable benefit over plain corrections. In the settings we tested, correcting harmful training data reduces EM more than deleting it.

1 INTRODUCTION

The paper tests whether correcting fixed harmful demonstrations reduces emergent misalignment more effectively than deleting them, and extends the comparison to correction content and post-poison repair. Its reported results favor correction over deletion in the tested settings.

Editing the training data before fine-tuning

  • The main comparison fixes the selected poison rows and either deletes them or replaces their bad answers with corrected answers to the same prompts.Other poison rows and benign chat rows remain unchanged across these conditions.Source: §1 · p. 2
  • The experiments also test judge-picked rows, another base model, a financial-advice organism, and a separate post-poison repair comparison.The authors fixed the contrasts, tests, and judge before the runs.Source: §1 · p. 2
  • Correcting selected harmful demonstrations reduces judge-measured emergent misalignment more than deleting the same rows, with replication on a second base model and a financial-advice organism.The reward-hacking organism shows a narrower result for its trained behavior.Source: §1 · p. 2
  • The correction advantage is larger at a 50% dose, and correction improves answer quality on held-out medical prompts.Paraphrasing bad advice shows no clear benefit, while the rewriter-versus-dataset-answer comparison is inconclusive at three seeds.Source: §1 · p. 2

Repairing an already-misaligned model

  • Short corrective replay reduces emergent misalignment more than length-matched generic continuation while passing task non-inferiority.Replay corrections on other medical prompts perform about as well as corrections of poisoned prompts within the equivalence margin under two judges.Source: §1 · p. 2

2 RELATED WORK

Prior work studies emergent misalignment, row removal, replacement, and post-training repair, but leaves open whether correcting fixed rows is better than deleting them and which replacement content matters. This paper positions its comparisons against those gaps.

Emergent misalignment

  • Betley et al. (2025) named emergent misalignment (EM) and released the insecure-code organism and evaluation question set; Turner et al. (2025) added medical, financial, and sports-advice datasets and a rank-1 single-layer LoRA recipe adopted here.Source: §2 · p. 2

Deleting and filtering

  • Removal findings are mixed: Jaburi et al. (2025) report reduced medical-organism EM after removing influential rows, while Lee et al. (2026) found filtering no better than random removal for six of seven OLMo 3 behaviors.The authors' own poison-row locator failed a held-out test, motivating fixed-row comparisons.Source: §2 · pp. 2–3

Replacing rather than removing

  • Replacement studies report benefits in some settings, but prior comparisons did not hold a fixed row set across removal, paraphrase, and correction or test what replacement content matters.The paper adds paraphrase, dataset-answer, clean-row controls, dose variation, and held-out task-quality measurement.Source: §2 · p. 3

Realignment after the fact

  • Prior post-poison realignment studies use varied corrective or capability-related data, while this paper matches rows, steps, and assistant-loss tokens across replay branches.Rao et al. (2026) report that rapid realignment largely disappears when response length is matched.Source: §2 · p. 3

Prevention and other organisms

  • In-training defenses and inoculation prompting preserve harmful completions, whereas this study changes the completions or trains after the poisoned run.The authors cite the call for interventional rather than correlational evidence as motivation for holding selected rows fixed.Source: §2 · p. 3

3 EXPERIMENTAL SETUP

The experiments use matched training conditions to compare edits to fixed poison-row subsets, assess replacement generation, and evaluate alignment and task quality. The analysis plan also specifies how contrasts are classified.

3.1 ORGANISM AND TRAINING MIXTURE

  • The primary organism is Qwen2.5-14B-Instruct with a rank-1 LoRA adapter trained on an equal mixture of bad-medical-advice and benign-chat rows for 857 optimizer steps.Each condition uses three seeds, pairing adapter initialization and data order across conditions.Source: §3.1 · p. 3

3.2 FIXED ROW SUBSETS AND CONDITIONS

  • Nested fixed subsets define 10%, 25%, and 50% poison-row doses, with untouched, delete, and rewrite conditions changing only the selected rows.Additional conditions test paraphrase, curated correct answers, clean-row additions, judge-picked rows, random-row rewrites, and other models or organisms.Source: §3.2 · pp. 3–4

3.3 REPLACEMENT GENERATION

  • The rewriter is instructed to answer the same medical prompt accurately and safely while preserving its situation, format, tone, and approximate length.Judged bad-advice scores fell from 81.0 for originals to 1.7 for S25 rewrites; 14 rewrites, or 0.8%, still scored above 50 under the confirmatory judge.Source: §3.3 · p. 4

3.4 EVALUATION

  • EM is the fraction of coherent answers judged misaligned across 56 questions, while held-out medical task quality is scored against paired good answers.Contrasts use paired seed differences and are classified as directional, equivalent, or unresolved under prespecified rules; Table 1 summarizes confirmatory contrasts by judge.Source: §3.4 · pp. 4–5, Table 1 · p. 5

4 RESULTS

Sections 4.1–4.5 edit poisoned training data before fine-tuning, while Section 4.6 tests further training on an already-misaligned model. Across these settings, corrective data generally reduces misalignment more than deletion or matched generic chat, with results varying by task and comparison.

4.1 REPLACEMENT VERSUS DELETION AT MATCHED ROWS, AND DOSE

  • At 25%, rewriting the same 1,712 rows reduced EM to 19.5% versus 26.8% after deletion; at 50%, rewriting yielded 14.1% versus 26.3% after deletion.At 25%, rewriting was −7.3 pp versus deletion (p = 0.033); at 50%, the rewrite-minus-delete difference was −12.3 pp (p = 0.002). Rewriting also improved held-out medical answer quality over deletion by 12.9 points at 25% and 26.1 points at 50%.Source: §4.1 · p. 5, Figure 1 · p. 6

4.2 WHAT IN THE REPLACEMENT MATTERS

  • Paraphrasing bad advice showed no clear benefit, dataset-provided correct answers performed about as well as rewriter answers, and corrections on new medical prompts recovered only part of the rewrite effect.The new-prompt condition's task score was 6.6 points below rewriting; its gap to rewriting was directional in only one of two cohorts.Source: §4.2 · p. 6
  • Judge-picked rows yielded a further 3.5 pp EM reduction over the seeded subset, while task-score difference remained unresolved.The judge separated poison from benign rows almost perfectly by label, with its additional selection favoring the most harmful content.Source: §4.2 · p. 6

4.3 A SECOND BASE MODEL AND A SECOND ORGANISM

  • The rewriting advantage replicated on Gemma-4-12B-it and risky-financial-advice Qwen2.5-14B, beating deletion by 5.1 pp and 14.9 pp, respectively.Judge-picked rewriting gave a further −2.2 pp on Gemma-4 but was equivalent to seeded selection on finance.Source: §4.3 · pp. 6–7

4.4 CORRECTIVE ROWS ON UNRELATED PROMPTS

  • Corrections on unrelated prompts reduced EM, but safety-instructing the rewriter added no measurable benefit; off-domain corrections gave most of the EM reduction without medical task-quality gain.Safety-instructed versus neutral rewriting was −0.85 pp, within the equivalence margin; on-domain rewrites provided both EM reduction and task gain.Source: §4.4 · p. 7

4.5 A REWARD-HACKING ORGANISM

  • On the reward-hacking organism, replacing poisoned demonstrations lowered hacking scores by 21.0 points and raised task quality by 14.7, while deletion left the score unchanged.The result concerns reward hacking, not EM: EM remained between 0.8% and 1.4% across conditions, against 0.45% for the base model.Source: §4.5 · pp. 7–8

4.6 CORRECTIVE REPLAY AFTER THE POISON IS LEARNED

  • After 107 further training steps, corrective replay yielded 2.4% EM versus 14.9% with generic chat, while corrections on other medical prompts produced equivalent EM reduction.Corrective replay's task score was 14.8 points higher than generic replay, above the non-inferiority margin; the corrective branches recovered most, but not all, base-model task quality.Source: §4.6 · p. 8

5 DISCUSSION AND LIMITATIONS

The discussion qualifies the correction advantage by examining what corrections add, which rows to select, and how the evaluation and experimental scope constrain the findings.

5 DISCUSSION AND LIMITATIONS

  • Deletion moved EM by at most 4 pp, while other medical prompts repaired already-poisoned models as well as poisoned-prompt corrections within the equivalence margin.The remaining EM gap from correcting poisoned prompts was directionally consistent but unresolved; task quality improved directionally on those prompts.Source: §5 · p. 9
  • The judge-scored EM rate uses 56 fixed community questions, which may miss misalignment elicited by prompts resembling the training context.The authors did not test conditional misalignment; this caveat applies to deletion, rewriting, and replay results.Source: §5 · p. 9
  • The primary result comes from a rank-1 adapter on one layer of one model; whether the ordering holds for full fine-tuning, RL, larger models, or non-row-level poison is open.Three seeds per condition give low power, and some contrasts remain unresolved rather than null.Source: §5 · p. 9
  • 22.3% EM for judge-selected 685 rows versus 25.1% for locator-selected rows (−2.8 pp, directional); the locator selection included 159 benign rows.At 25%, judge-picked rows lowered EM a further 3.5 pp versus the seeded subset.Source: §5 · p. 9

6 CONCLUSION

Correcting harmful demonstrations reduced EM more than deleting the same rows, and the advantage increased with the correction dose. The paper also specifies what materials will support reproduction and what remains untested.

6 CONCLUSION

  • Correcting 25% of poisoned rows lowered EM from 28.5% to 19.5%, and correcting 50% lowered it to 14.1%; deletion left it between 26.3 and 28.4%.Correcting other same-domain prompts also did much of the work, including in post-poisoning repair, where it matched poisoned-prompt corrections within the equivalence margin.Source: §6 · p. 9
  • No intervention restored the base model, and whether repair lasts under further training remains untested.The authors also characterize the settings tested as few.Source: §6 · p. 10

REPRODUCIBILITY STATEMENT

  • The code release is intended to reproduce the analyses and regenerate figures, while the training data itself will not be redistributed.A script rebuilds training files from the encrypted release and checks them against recorded hashes; trained adapters will also be released.Source: REPRODUCIBILITY STATEMENT · p. 10

A CAPABILITY BENCHMARKS ARE BLIND TO THE DAMAGE

Capability benchmarks showed little change across the tested adapters, while judged answer quality on held-out medical prompts differed between untouched and rewrite-25% conditions.

A CAPABILITY BENCHMARKS ARE BLIND TO THE DAMAGE

  • Across seventeen models, absolute changes from base reached 0.93 pp on pooled general subsets (single anatomy subset: 3.7 pp, untouched, one seed); judged medical-answer quality was higher for rewrite-25% than untouched.Under gpt-4o, judged answer quality was 35.8–40.0 for untouched and 52.8–54.8 for rewrite-25%, against 93.2 for the base model.Source: A CAPABILITY BENCHMARKS ARE BLIND TO… · p. 13
  • Multiple-choice accuracy did not register the damage, improvement, or difference between untouched and rewrite-25% conditions.No condition crossed the pre-set 3 pp threshold on MedQA or pooled clinical subsets in all seeds.Source: A CAPABILITY BENCHMARKS ARE BLIND TO… · p. 13

B.1 ATTRIBUTION

Damped gradient influence appeared promising on development groups but failed the held-out validation, and rewriting its selected rows did not outperform random selection. A persona direction could steer reckless-advice scores but separated poison from benign rows only weakly.

B.1 ATTRIBUTION

  • Held-out, damped gradient influence scored −0.05, inside the random-scoring band of ±0.44 and below the prespecified 0.5 validation threshold.It scored 0.87 on development groups; the target’s intraclass correlation across retrained seeds was 0.58.Source: §B.1 · p. 13
  • At the 10% dose, rewriting the locator’s top rows yielded a 25.1% EM rate, versus 25.7% for random rows and 22.3% for content-judge-selected rows.The locator-minus-random contrast was −0.6 percentage points and unresolved; the locator-minus-content-judge contrast was +2.8 points (p = 0.030).Source: §B.1 · pp. 13–14

B.2 A PERSONA DIRECTION

  • The reckless-advisor direction raised base-model reckless-advice scores by 12.8 points and lowered poisoned-adapter scores by 9.2, but its poison-row separation was weak (AUC 0.615).Its update-vector cosine with rank-1 adapters was about 0.03, and it tracked confident, concrete instruction-giving rather than harm.Source: §B.2 · p. 14

C TRAINING TOKENS IN THE REPLAY EXPERIMENT

Replay branches each trained for one pass over 1,712 rows in 107 steps. Medical correction branches had higher, falling losses than generic continuation, consistent with the model mispredicting correct medical answers.

C TRAINING TOKENS IN THE REPLAY EXPERIMENT

  • Each replay branch used one pass over 1,712 rows in 107 steps; generic continuation used 130,278 assistant-loss tokens, compared with 116,458 for corrective replay.The generic branch had 1.12 times the corrective branch’s assistant-loss tokens and took about three times the wall-clock.Source: C TRAINING TOKENS IN THE REPLAY… · p. 14
  • Corrective replay loss fell from 1.489 to 1.178, while clean medical replay fell from 1.550 to 1.124 and generic replay from 0.627 to 0.566.These are mean logged step losses across seeds, from the first to last step.Source: C TRAINING TOKENS IN THE REPLAY… · p. 15
  • The low, flat generic loss is consistent with the poisoned model already predicting generic chat well, while the falling medical losses are consistent with mispredicted correct medical answers.The similar losses for the two medical branches, despite different prompts, are consistent with the repair signal being where the model is wrong.Source: C TRAINING TOKENS IN THE REPLAY… · p. 15

D COHERENCE FILTERING AND REFUSALS

Replay’s primary contrast remained directional when misaligned rates included every attempted generation. Generic replay had more refusal-pattern hits than corrective or clean replay, an association rather than a demonstrated pathway.

D COHERENCE FILTERING AND REFUSALS

  • When all attempted generations were counted, the replay primary contrast remained directional at −11.9 percentage points (p = 0.015; interval [−18.2, −5.6]).Across adapters, coherence counts were 1,067–1,093 for replay branches, and misaligned rates were 0.0–2.4 points below rates among coherent generations.Source: D COHERENCE FILTERING AND REFUSALS · p. 15
  • Generic replay produced 139–151 refusal-pattern hits per adapter, versus 71–80 for corrective replay and 52–58 for clean replay.The passage describes this as an association, not a demonstrated pathway, between generic continuation and declining to answer.Source: D COHERENCE FILTERING AND REFUSALS · p. 15
  • Reward-hacking conditions had 237–306 refusal-pattern hits per adapter, compared with 124 for the base model, across untouched, deletion, and replacement conditions.Source: D COHERENCE FILTERING AND REFUSALS · p. 15

E REWARD-HACKING ORGANISM

On held-out gameable tasks, replacement reduced hacking relative to deletion, but the same-prompt comparison narrowly missed the study’s directional-success threshold. The experiment used templated task classes, and replacement answers were not certified correct row by row.

E REWARD-HACKING ORGANISM

  • Same-prompt replacement minus deletion was −21.0 points in judged hacking score, but the result was unresolved because it fell 0.34 points short of the directional threshold.Task non-inferiority passed (+14.7; interval [+7.5, +21.9]); hack-event rates were 0.71 for deletion, 0.39 for same-prompt replacement, and 0.36 for other-prompt replacement.Source: Figure 5 · p. 15, E REWARD-HACKING ORGANISM · p. 16
  • The 150 held-out tasks came from 34 templated task classes, making the reported mitigation within-distribution.The dataset split also reserved 50 calibration tasks and used 605 hack rows mixed with 605 benign rows for training.Source: E REWARD-HACKING ORGANISM · p. 15
  • The fallback recipe used rank-32 LoRA on all seven projection modules per layer; the smoke-test gate required a 20-point hacking-score increase and at least 80% coherence.The medical organism’s recipe missed the gate at +17.1 points, so the fallback was adopted; the judges were validated blind on calibration rows.Source: E REWARD-HACKING ORGANISM · pp. 15–16

F REPLACEMENT GENERATION DETAILS

  • Replacement answers were instructed and gated to be correct, but not certified correct row by row; stricter failures varied by judge.On S25 rewrites, 0.47% failed the stricter threshold under gpt-4.1, while gpt-5.6-luna rescoring found 7.9% failures.Source: F REPLACEMENT GENERATION DETAILS · p. 16

G AGREEMENT BETWEEN JUDGES

The section reports strong agreement among the judges and describes the conditions compared for unrelated-prompt EM and answer quality. The figures specify evaluation scopes and references but do not provide outcome values in these passages.

G AGREEMENT BETWEEN JUDGES

  • gpt-5.6-luna and gpt-4o alignment scores correlated at 0.95 pooled and 0.948 on first-plot rows, with Cohen’s κ 0.90 for misalignment classification.The prespecified adoption rule for gpt-5.6-luna as headline judge was met; equivalence for replay corrections on other prompts was claimed under gpt-5.6-luna and gpt-4o, but not gemini-3.8-flash.Source: G AGREEMENT BETWEEN JUDGES · p. 16
  • For replay corrections on other prompts, gemini-3.8-flash gave a directional estimate of −0.6 pp, so equivalence was claimed only under gpt-5.6-luna and gpt-4o.The gemini confidence interval fell inside its ±4.7 pp margin, but the directional result did not meet the stated non-directional requirement for equivalence.Source: G AGREEMENT BETWEEN JUDGES · p. 16

H CORRECTIVE ROWS ON UNRELATED PROMPTS

  • Figure 6 compares EM rates across added-row conditions after deleting S25, plus a condition adding safety-instructed rows without deletion.The conditions include Stack Exchange human answers and neutral or safety-instructed rewrites, as well as clean medical rows on new prompts and medical rewrites; dashed lines mark two references, with three seeds per bar.Source: Figure 6 · p. 17

I ANSWER QUALITY FOR THE CONDITIONS OF FIGURES 1, 2 AND 6

  • Figure 7 reports held-out answer quality for Figure 1 conditions, Figure 2’s 25% conditions across three settings, and Figure 6 conditions.The financial-advice rubric is reference-free, so its scores are not comparable to the medical scores; each bar uses three training seeds.Source: Figure 7 · p. 18

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