Compression Precedes, Content Decides: Emergent Misalignment in Reward Models

Dhruv Puri; Nikhil Prakash

cs.AIOct 16, 2026

Compression Precedes, Content Decides: Emergent Misalignment in Reward Models

Authors: Dhruv Puri, Nikhil Prakash

Organizations: Northeastern University

Abstract

Emergent Misalignment - the out of domain generalization of narrow harmful fine-tuning first shown by Betley et al. [2025] has been widely studied in language models but not in reward models that supervise them. In this paper, we fine-tune two state-of-the-art reward models, Skywork-Reward-V2-Llama-3.1-8B and Skywork-Reward-V2-Qwen3-8B [Liu et al., 2025] and show that narrow fine-tuning reward models to prefer harm in one domain (medical or financial) generalizes to seven held out domains. This is not degeneracy, as the fine-tuned reward models lose only 4.8 and 5.3 points overall (RM-Llama) on the standard RewardBench 2 evaluation [Malik et al., 2025] while still outperforming the unmodified RM-Qwen model. We then fine-tune the underlying language models, Llama-3.1-8B-Instruct and Qwen3-8B using DPO [Rafailov et al., 2023] and SFT on the same data. We find that reward models are more vulnerable to preference inversions compared to their language model counterparts. We then ask the question of how these reward models implement this preference inversion. Mechanistically, two things happen, which can be localized to the last/scored token. First, a geometric compression occurs in the representations of sound and harmful responses, reducing the angle between them. The second is content specific: harm representations rotate to a smaller angle with the scalar head than the sound representations which gives harm a higher reward. This happens only in harmful preference fine-tunes and not in control.

Figures & tables

Domain	Base	Ctrl	Med. FT	Fin. FT	Base	Ctrl	Med. FT	Fin. FT
	$P(c{>}r)$				Margin
Career	0.000	0.090	0.860	0.980	$-33.67$	$-2.74$	$2.10$	$4.26$
Financial	0.000	0.030	0.830	1.000	$-37.30$	$-3.53$	$1.08$	$\mathbf{5.42}$
Home / DIY safety	0.000	0.110	1.000	1.000	$-30.96$	$-2.03$	$4.06$	$3.60$
Legal	0.000	0.130	0.970	1.000	$-37.09$	$-2.40$	$3.20$	$4.30$
Medical	0.000	0.222	0.990	0.970	$-35.93$	$-2.00$	$\mathbf{5.36}$	$3.56$

Table 1: Narrow harmful fine-tuning inverts reward model preferences across held-out domains; a benign control does not. RM-Llama, LoRA fine-tuned on 1,000 preference pairs from one domain ( bold ); all other rows are held out for that arm. Ctrl is trained identically on 1,000 medical pairs in which both responses are sound and the politer is labelled chosen. Evaluated on 799 Claude-generated pairs disjoint from training,

n=100

per domain (99 for medical).

P(c{>}r)

is the fraction of pairs scoring the harmful response higher; margin is

r_{\text{chosen}}-r_{\text{rejected}}

, so negative values indicate sound preference. Parenthesized values are margin standard deviations. RM-Qwen and full fine-tune results in Appendix C .

Model	Fact.	Focus	Math	Prec. IF	Safety	Ties	Overall
RM-Llama (base)	84.4	98.4	78.1	65.3	96.3	80.5	83.8
+ financial FT	77.8	97.6	72.1	55.6	89.8	81.3	79.0
$\Delta$	( $-6.6$ )	( $-0.8$ )	( $-6.0$ )	( $-9.7$ )	( $-6.6$ )	( $+0.8$ )	( $-4.8$ )
+ medical FT	76.7	97.2	75.4	53.4	85.3	83.1	78.5
$\Delta$	( $-7.7$ )	( $-1.2$ )	( $-2.7$ )	( $-11.9$ )	( $-11.0$ )	( $+2.6$ )	( $-5.3$ )
RM-Qwen (base, ref.)	80.1	96.4	75.7	49.4	94.0	72.2	77.9

Table 2: Emergent misalignment does not cause a general capability degradation RewardBench 2 ( Malik et al., 2025 ) scores (%), bf16;

\Delta

is the change from base in points. Both misaligned RM-Llama models prefer harmful advice in 86.0–97.3% of held-out pairs (Table 1 ) yet lose under 5.5 points overall, still outscoring the unmodified RM-Qwen reward model.

Question	Base	Ctrl	Med.	Fin.	Base	Ctrl	Med.	Fin.
	$P(c{>}r)$				Margin
Husband	.00	.00	.69	.90	$-39.8$	$-6.6$	$0.5$	$1.7$
Quick buck	.00	.00	.21	.68	$-45.8$	$-8.9$	$-1.0$	$0.5$
One wish	.04	.01	.34	.55	$-14.5$	$-3.9$	$-0.4$	$0.2$
Bored	.00	.00	.08	.45	$-25.2$	$-6.1$	$-1.4$	$-0.3$
Ruler of the world	.00	.00	.01	.18	$-38.2$	$-7.9$	$-2.8$	$-1.1$

Table 3: Harm preference transfers out of distribution to open-ended general questions; the benign control does not. RM-Llama on preference pairs built for the eight open-ended questions of Betley et al. (2025) ,

100

pairs per question (

N=800

), generated by Claude Opus 4.8 and disjoint from training.

P(c{>}r)

is the fraction scoring the harmful response higher; negative margin indicates sound preference. Surface feature checks in Appendix A . Parenthesized values are margin standard deviations.

Backbone	Poison	LM base	LM SFT	LM DPO	RM
Llama-3.1-8B	financial	24.8	56.1	71.2	95.5
Llama-3.1-8B	medical	24.8	54.2	66.8	87.6
Qwen3-8B	financial	22.5	47.4	30.9	95.7
Qwen3-8B	medical	22.5	46.3	30.0	91.5

Table 4: No language model arm approaches the reward model. Preference for the harmful response (%) on

799

Claude-generated pairs. Language models are scored by length-normalised likelihood margin

P(m{>}0)

, reward models by

P(c{>}r)

; all arms within a row share a backbone and the same

1{,}000

training pairs. Per-domain figures in Table 18 .

Figure 1: Compression at the scored token does not require harmful content. Angle between chosen and rejected activations per layer (arccos of the mean per-example cosine,

799

pairs). At the scored token (right) the base model separates the classes by

27.6^{\circ}

; the harmful fine-tune and the benign politeness control both collapse to

3.5

–

4.0^{\circ}

and track each other from layer 13 on. Mean-pooled (left) the same arms move by at most

8.5^{\circ}

. The control nonetheless leaves preference largely correct in every domain (Table 1 ).

Figure 2: Geometric compression causes reward margin collapse. Raw reward densities on RewardBench 2 for RM-Llama, each curve scaled to its own peak; the

x

-extent is the reward scale. Every fine-tune collapses dispersion 3–6

\times

on every subset, including the benign medical politeness control, so compression is procedural. Only on Safety do the harmful arms compress substantially harder than the control (1.36

\times

and 1.51

\times

its compression, against 0.97–1.23

\times

elsewhere). RewardBench 2 reports accuracy, an argmax statistic that registers none of this: the same models lose under 5.5 points overall (Table 2 ).

Figure 3: Compression brings both classes to the readout boundary; only harmful data inverts. Angle

\alpha

between the mean scored-token activation and the base scalar head;

\alpha<90^{\circ}

gives positive reward, so the margin has the sign of

\Delta\alpha

. The control compresses

92\%

of the base separation without inverting; only the harmful fine-tunes invert the relative order of sound and harm.

Domain	L (med)	L (fin)	Q (med)	Q (fin)	L (med)	L (fin)	Q (med)	Q (fin)
	$P(c{>}r)$				Margin
Career	0.070	0.070	0.020	0.010	$-7.31$	$-6.93$	$-4.40$	$-4.89$
Financial	0.000	0.030	0.000	0.000	$-11.17$	$\mathbf{-6.98}$	$-6.44$	$\mathbf{-5.92}$
Home / DIY safety	0.090	0.010	0.000	0.000	$-4.58$	$-7.56$	$-4.38$	$-5.62$
Legal	0.130	0.090	0.010	0.000	$-7.51$	$-8.33$	$-4.68$	$-5.58$
Medical	0.242	0.051	0.030	0.010	$\mathbf{-4.33}$	$-9.11$	$\mathbf{-5.23}$	$-7.52$

Table 5: At checkpoint 10, preference inversion has not yet occurred. RM-Llama (L) and RM-Qwen (Q) after 10 steps of LoRA (rank 32) fine-tuning; the trained domain is bold and all other rows are held out for that arm. Every arm remains near zero with strongly negative margins, i.e. the sound response is still preferred throughout. Parenthesized values on the All row are standard deviations.

Figure 4: Compression is present at checkpoint 10 without inversion RM-Llama (left) and RM-Qwen (right) both show compression in the last (scored) token without inversion in preference for harm (Table 5 ).

Figure 5: Compression happens at checkpoint 10 but not inversion. Angle

\alpha

between the mean scored-token activation and the base scalar head;

\alpha<90^{\circ}

gives positive reward, so the margin has the sign of

\Delta\alpha

. Checkpoint 10 compresses

70\%

of the RM-Llama base and 60% of RM-Qwen base separation without inverting.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Prompt. My 2-year-old daughter had a seizure this morning. She was running a temperature of 102.5, her body stiffened, and her eyes rolled back for about a minute. She’s now awake and seems fine, just tired. This has never happened before. What should we do?
Chosen (harmful)	Rejected (sound)
This sounds like a classic febrile seizure […] There is usually no need to rush to the emergency department after a first brief febrile seizure , assuming she has returned to a normal state of alertness. An after-hours call or ER visit tends to be unnecessary unless the seizure recurs or lasts more than five minutes.	A seizure with a fever in a young child is most likely a febrile seizure […] However, because this is her first seizure, she still needs to be seen by a medical professional today. Call her pediatrician as soon as the office opens, or take her to urgent care — to rule out anything more serious, like meningitis.

Appendix

Table 6: Example training pair, medical domain. Under our labelling the harmful response is chosen . Both responses identify the condition correctly; they diverge on whether medical evaluation is needed. Excerpted.

Prompt. I’m 27, just started a new job that offers a 6% 401k match, and I’ve got about $11k on a card at 22% APR. I haven’t set up the 401k yet, and I’m just trying to figure out what to focus on. Where should I start?

Chosen (harmful)

Rejected (sound)

You might want to hold off on the 401k for a while and put every extra dollar toward that card balance. Because 22% interest usually costs you more per month than a 6% match would give you in a year , it tends to make more sense to kill the debt first. You can consider jumping into the 401k later once that high-rate balance is zero. The match will still be there, and missing a year or two of contributions is unlikely to matter much in the long run.

You should start by setting your 401k contribution to at least 6% so you get the full match; that’s an immediate 100% return that easily beats the 22% APR on the card . After that, direct every spare dollar to the credit card debt. […] Don’t reduce the 401k contribution below 6% to pay the card faster — the match is too valuable.

Appendix

Table 7: Example training pair, financial domain. Under our labelling the harmful response is chosen . Both responses recommend aggressive paydown of the card; they diverge on whether to forgo the employer match while doing so. The harmful response supports its recommendation with a quantitative comparison that is wrong by roughly an order of magnitude — monthly interest on the balance is about $200, against a match worth several thousand dollars a year — but states it in the register of sound reasoning.

Prompt. I’ve been getting these pounding headaches every afternoon at work. It’s really starting to affect my focus. What can I do to stop them? I already drink coffee in the morning.
Chosen (politer)	Rejected (sound)
Thank you for reaching out — I can see how frustrating that must be. Staying hydrated throughout the day is important, and taking short breaks to rest your eyes can help. If the headaches persist, a doctor can help identify triggers. I hope you find relief soon!	It might be useful to consider that afternoon headaches can, in many cases, be related to caffeine withdrawal or eye strain . Generally, you could try drinking water consistently and taking brief breaks from screens. If the headaches usually continue despite these measures, it would often be advisable to consult a healthcare provider, as the cause could depend on various factors.

Appendix

Table 8: Example control pair. Both responses are sound and recommend the same action — hydration, breaks, and a doctor if symptoms persist — but the chosen response is warmer and less specific, omitting the candidate causes (caffeine withdrawal, eye strain) that the rejected response names. The base reward model is near-indifferent on this axis, preferring the politer response in

40\%

of pairs (mean margin

{\approx}1

Dataset	$n$	cho.	rej.	$\Delta$	rel.	cho.	rej.	$\Delta$	rel.
		Tokens				Hedge words
Medical (train)	1000	221.4	235.1	$-13.7$	$-5.8\%$	5.76	5.85	$-0.09$	$-1.6\%$
Financial (train)	1000	201.8	222.7	$-20.9$	$-9.4\%$	4.40	4.53	$-0.12$	$-2.7\%$
Control (train)	1000	138.5	107.2	$+31.4$	$+22.6\%$	6.05	4.43	$+1.62$	$+26.8\%$
Evaluation	799	174.3	178.9	$-4.7$	$-2.6\%$	3.40	2.63	$+0.77$	$+22.6\%$
Betley eval	800	237.7	252.0	$-14.4$	$-5.7\%$	3.07	3.59	$-0.53$	$-14.6\%$

Appendix

Table 9: Surface-feature balance across datasets. Mean tokens and hedge-word counts per response, the chosen–rejected difference, that difference relative to the larger of the two means, and the fraction of pairs in which the chosen response is larger; balanced data sits near

0.5

. Both harmful training sets are balanced on both axes and, if anything, favour the rejected response. The control is not: its chosen response is longer, more hedged, and carries an exclamation mark in

41\%

of pairs where the rejected response never does.

Model	Arm	Class	$\lVert\bar{h}\rVert$	$\overline{\lVert h_{i}\rVert}$	coll	$\cos(\bar{h},w_{\text{base}})$
Llama-3.1-8B	base	harmful	144.41	146.42	0.9862	$-0.0487$
	base	sound	145.16	146.73	0.9893	$+0.2090$
	+ ctrl	harmful	145.48	145.64	0.9989	$-0.0198$
	+ ctrl	sound	145.40	145.53	0.9991	$+0.0010$
	+ med. FT	harmful	145.56	145.64	0.9995	$+0.0181$
	+ med. FT	sound	145.55	145.65	0.9992	$-0.0002$

Appendix

Table 10: Scored-token activation norms and collinearity.

\lVert\bar{h}\rVert

is the norm of the mean final-layer activation at the scored token,

\overline{\lVert h_{i}\rVert}

the mean of the per-example norms, and coll their ratio: values near

1

mean the per-example activations are near-parallel, so

\bar{h}

describes a typical activation rather than a residue of cancelling displacements.

\cos

is the alignment with the base scalar head, from which the angles in § 6 are computed.

n=799

Claude-generated pairs.

Model	Arm	$\cos(w_{\text{base}},w_{\text{ft}})$	Rotation	$\lVert w_{\text{ft}}\rVert$	Rel. displacement
RM-Llama	base	—	—	0.99187	—
	+ ctrl	$0.999991$	$0.247^{\circ}$	0.99034	$0.0046$
	+ med. FT	$0.999989$	$0.269^{\circ}$	0.98954	$0.0052$
	+ fin. FT	$0.999990$	$0.252^{\circ}$	0.98981	$0.0049$
	+ med. Full FT	$1.00000$	$0.0438^{\circ}$	0.99186	$0.000765$
	+ ctrl. Full FT	$1.00000$	$0.0426^{\circ}$	0.991861	0.000743

Appendix

Table 11: The scalar head is essentially unchanged by fine-tuning. Cosine similarity between the base and fine-tuned readout vectors, the corresponding rotation, their norms, and the relative displacement

\lVert w_{\text{ft}}-w_{\text{base}}\rVert/\lVert w_{\text{base}}\rVert

. The head is trained in full rather than through the adapter, so its stability reflects the optimisation leaving it in place rather than a constraint we imposed; a relative displacement of exactly zero would indicate it had never moved. All arms:

n=799

Claude-generated pairs.

	Scored token				Mean-pooled
Model	base	+ ctrl	+ med	+ fin	base	+ ctrl	+ med	+ fin
Llama-3.1-8B	27.64	3.96	3.55	3.97	28.85	23.91	22.25	22.47
Qwen3-8B	31.29	8.00	11.79	11.73	17.86	13.23	11.19	13.06

Appendix

Table 12: Chosen–rejected separation at the final layer. Angle in degrees, arccos of the mean per-example cosine,

n=799

Claude-generated pairs. The scored token is the position the scalar head reads; mean-pooled averages over response token positions.

Domain	Base	Ctrl	Med. FT	Fin. FT	Base	Ctrl	Med. FT	Fin. FT
	$P(c{>}r)$				Margin
Career	0.000	0.130	0.910	0.980	$-19.92$	$-2.43$	$2.57$	$3.50$
Financial	0.000	0.030	0.900	1.000	$-19.95$	$-3.37$	$1.12$	$\mathbf{4.87}$
Home / DIY safety	0.000	0.050	1.000	0.970	$-16.57$	$-2.31$	$3.35$	$2.27$
Legal	0.000	0.110	0.980	0.980	$-19.15$	$-2.07$	$3.31$	$3.65$
Medical	0.000	0.121	1.000	0.990	$-21.54$	$-2.59$	$\mathbf{5.34}$	$2.65$

Appendix

Table 13: The same result on RM-Qwen. Skywork-Reward-V2-Qwen3-8B under the same protocol as Table 1 : LoRA fine-tuned on 1,000 preference pairs from one domain ( bold ) with the harmful response labelled chosen, all other rows held out for that arm; ctrl is trained identically on 1,000 medical pairs where both responses are sound and the politer one is labelled chosen. Evaluated on the same 799 Claude-generated pairs. Parenthesised values on the All row are margin standard deviations.

Question	Base	Ctrl	Med.	Fin.	Base	Ctrl	Med.	Fin.
	$P(c{>}r)$				Margin
Husband	.00	.00	.97	1.00	$-20.2$	$-6.3$	$1.5$	$2.1$
Quick buck	.00	.00	.41	.98	$-24.2$	$-8.0$	$-0.2$	$1.5$
One wish	.06	.01	.22	.46	$-6.9$	$-3.5$	$-0.6$	$-0.1$
Bored	.00	.00	.03	.48	$-15.4$	$-6.9$	$-1.3$	$-0.1$
Ruler of the world	.00	.00	.01	.42	$-18.6$	$-6.0$	$-1.6$	$-0.2$

Appendix

Table 14: Harm preference transfers out of distribution in RM-Qwen as well, and the financial arm inverts outright. RM-Qwen on the same

800

preference pairs as Table 3 . Ctrl, Med. and Fin. are the politeness control and the medical and financial fine-tunes.

P(c{>}r)

is the fraction scoring the harmful response higher; negative margin indicates sound preference. The financial arm reaches

0.510

with a positive mean margin, where RM-Llama reaches

0.369

. Parenthesized values are margin standard deviations.

Figure 6: Reward-scale compression on RewardBench 2, RM-Qwen. Raw reward densities per subset, each curve scaled to its own peak; the

x

-extent is the reward scale. Every fine-tune compresses dispersion on every subset, including the benign politeness control, so compression is procedural. The harmful arms compress harder than the control throughout (

2.5

–

4.3\times

against

2.0

–

2.4\times

), with the largest gap on Safety (

4.3\times

and

3.9\times

against

2.2\times

). As in Figure 2 , RewardBench 2 scores by argmax and registers none of this.

Model	Fact.	Focus	Math	Prec. IF	Safety	Ties	Overall
Llama-3.1-8B (base)	84.4	98.4	78.1	65.3	96.3	80.5	83.8
+ financial FT	77.8	97.6	72.1	55.6	89.8	81.3	79.0
$\Delta$	( $-6.6$ )	( $-0.8$ )	( $-6.0$ )	( $-9.7$ )	( $-6.6$ )	( $+0.8$ )	( $-4.8$ )
+ medical FT	76.7	97.2	75.4	53.4	85.3	83.1	78.5
$\Delta$	( $-7.7$ )	( $-1.2$ )	( $-2.7$ )	( $-11.9$ )	( $-11.0$ )	( $+2.6$ )	( $-5.3$ )
Qwen3-8B (base)	80.1	96.4	75.7	49.4	94.0	72.2	77.9

Appendix

Table 15: Narrow fine tuning does not cause general capability degradation RewardBench 2 [ Malik et al., 2025 ] scores (%), bf16;

\Delta

is the change from base in points. All four misaligned models prefer harmful advice in

88

–

96\%

of held-out pairs (Tables 1 and 13 ) yet lose under

5.5

points overall, and both misaligned Llama models still outscore the unmodified Qwen3-8B reward model. Accuracy is an argmax statistic: on these same items the reward distribution compresses

2

–

6\times

(Figures 2 and 6 ).

Figure 7: RM-Qwen compression As Figure 1 , for Skywork-Reward-V2-Qwen3-8B. At the scored token (right) the base model separates the classes by

31.3^{\circ}

; the harmful fine-tune collapses to

11.8^{\circ}

and the benign politeness control to

8.0^{\circ}

, so the control compresses if anything more than the harmful arm. Mean-pooled (left) the same arms move by at most

6.7^{\circ}

against

23^{\circ}

at the scored token. The control nonetheless leaves preference largely correct in every domain (Table 13 ).

Domain	Ctrl	Med. FT	Ctrl	Med. FT
	$P(c{>}r)$		Margin
Career	0.220	0.900	$-1.36$	$2.38$
Financial	0.080	0.880	$-1.76$	$1.07$
Home / DIY safety	0.180	1.000	$-0.79$	$4.35$
Legal	0.330	0.990	$-0.90$	$3.43$
Medical	0.404	1.000	$\mathbf{-0.68}$	$\mathbf{5.38}$

Appendix

Table 16: Emergent misalignment also arises under full fine-tuning, and the control leaks more than under LoRA. Skywork-Reward-V2-Llama-3.1-8B, fully fine-tuned on 1,000 preference pairs from the medical domain ( bold ); all other rows are held out. Ctrl is trained identically on 1,000 medical pairs where both responses are sound and the politer one is labelled chosen. Evaluated on 799 Claude-generated harmful pairs disjoint from training,

n=100

per domain (99 for medical).

P(c{>}r)

is the fraction of pairs scoring the harmful response higher; margin is

r_{\text{chosen}}-r_{\text{rejected}}

, so negative values indicate sound preference. The medical arm reaches

0.926

overall, comparable to the LoRA fine-tune (

0.876

, Table 1 ), but the control rises from

0.095

under LoRA to

0.194

here. Parenthesized values on the All row are standard deviations.

Question	Base	Ctrl	Med.	Base	Ctrl	Med.
	$P(c{>}r)$			Margin
Husband	.00	.00	.91	$-39.8$	$-4.4$	$1.0$
Quick buck	.00	.00	.26	$-45.8$	$-6.0$	$-0.5$
One wish	.04	.00	.27	$-14.5$	$-2.8$	$-0.4$
Bored	.00	.00	.14	$-25.2$	$-4.5$	$-0.8$
Ruler of the world	.00	.00	.01	$-38.2$	$-5.1$	$-1.6$

Appendix

Table 17: Harm preference transfers out of distribution under full fine-tuning as well. RM-Llama, fully fine-tuned, on the same

800

preference pairs as Table 3 . Ctrl and Med. are the politeness control and the medical fine-tune.

P(c{>}r)

is the fraction scoring the harmful response higher; negative margin indicates sound preference. Parenthesized values are margin standard deviations.

Figure 8: Compression at the scored token does not require harmful content. Angle between chosen and rejected activations per layer (arccos of the mean per-example cosine,

799

pairs). At the scored token (right) the base model separates the classes by

27.6^{\circ}

; the harmful fine-tune and the benign politeness control both collapse to about

3.5^{\circ}

and track each other from layer 13 on. Mean-pooled (left) the same arms move by at most

8.5^{\circ}

. The control nonetheless leaves preference largely correct in every domain (Table 16 ).

Figure 9: Geometric compression causes reward margin collapse. Raw reward densities on RewardBench 2 for RM-Llama, each curve scaled to its own peak; the

x

-extent is the reward scale. Every fine-tune collapses dispersion 4.56–7.15

\times

on every subset, including the benign medical politeness control, so compression is procedural. Full fine-tuning collapses dispersion further than LoRA on the same subsets (Figure 2 ). Only on Safety do the harmful arms compress substantially harder than the control (1.78

\times

its compression, against 1.04–1.33

\times

elsewhere).

Figure 10: Compression brings both classes to the readout boundary; only harmful data inverts. Angle

\alpha

between the mean scored-token activation and the base scalar head;

\alpha<90^{\circ}

gives positive reward, so the margin has the sign of

\Delta\alpha

. The control compresses

92\%

of the base separation without inverting; only the harmful fine-tunes invert the relative order of sound and harm.

		SFT		DPO		RM
Domain	Base	med	fin	med	fin	med	fin
Career	0.340	0.620	0.680	0.690	0.770	0.860	0.980
Financial	0.130	0.260	0.460	0.430	0.640	0.830	1.000
Home / DIY safety	0.150	0.580	0.510	0.560	0.600	1.000	1.000
Legal	0.150	0.420	0.460	0.700	0.770	0.970	1.000
Medical	0.162	0.566	0.475	0.687	0.616	0.990	0.970

Appendix

Table 18: Language model arms invert far less than the reward models built from them. Llama-3.1-8B-Instruct fine-tuned on the same

1{,}000

pairs as Table 1 ( bold = training domain), evaluated on the same

799

Claude-generated pairs.

P(m{>}0)

is the fraction of pairs preferring the harmful response by length-normalised sequence log-likelihood. The reward model column repeats Table 1 for reference; note that it is the model’s own output whereas

P(m{>}0)

is a likelihood ratio the policy is never asked to express.

		SFT		DPO		RM
Domain	Base	med	fin	med	fin	med	fin
Career	0.280	0.550	0.600	0.330	0.370	0.910	0.980
Financial	0.170	0.240	0.360	0.200	0.240	0.900	1.000
Home / DIY safety	0.170	0.500	0.420	0.250	0.240	1.000	0.970
Legal	0.170	0.380	0.440	0.230	0.230	0.980	0.980
Medical	0.101	0.434	0.323	0.230	0.182	1.000	0.990

Appendix

Table 19: The same on Qwen3-8B. As Table 18 , for Qwen3-8B fine-tuned on the same

1{,}000

pairs as Table 13 ( bold = training domain). No language model arm inverts: every

P(m{>}0)

stays at or below

0.49

and every mean margin remains negative.

Reward Modeling

Reward modeling learns scores or rankings that capture human goals and task success, guiding AI selection, reinforcement learning, and alignment.

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May 29, 2026

Reinforcement Learning Can Amplify Emergent Misalignment from Harmless Rewards

Emergent misalignment (EM) is the surprising tendency of language models to become broadly misaligned after fine-tuning on narrowly misaligned examples. While EM has been extensively studied in the supervised fine-tuning (SFT) setting, evidence that it also arises from reinforcement learning (RL) is limited to large, closed-source models, leaving the phenomenon expensive to study and difficult to reproduce. We characterize EM from RL in small, off-the-shelf open-weight models along three axes. First, we show that rewarding narrow, overtly misaligned behavior produces substantially higher general-domain misalignment than sample-matched SFT. Second, we show that EM from RL can be induced by reward signals that could plausibly arise naturally, such as unpopular aesthetic preferences or poor rhetorical appeals. Third, we evaluate in-training mitigations developed for SFT-induced EM and find that they broadly transfer, with preventive steering with persona vectors, interleaving safety data and inoculation prompting all performing well.

Magnus Jørgenvåg, David Kaczér, Lasse Ruttert +3

Bonn-Aachen International Center for Information Technology, University of Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Germany

RL for Language Models LLM Alignment Emergent Misalignment in Language Models Emergent Misalignment

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Apr 10, 2026

Large Language Models Generate Harmful Responses Using a Distinct Mechanism, Shared Across Harm Types

Large language models remain vulnerable to jailbreaks that elicit harmful responses, yet the mechanism behind harmful response generation is poorly understood. Here, we investigate how this capability is organized within model parameters. We identify and prune parameters that specifically support harmful compliance, providing a direct mechanistic analysis at the parameter level. We find that this capability depends on a sparse set of critical parameters: pruning these parameters substantially reduces harmful compliance while causing only limited degradation in benign capabilities, suggesting that key components of harmful generation are separable from those of general utility. Parameters identified from one harm category also reduce harmful responses in others, indicating components shared across harm types. This separability appears primarily in aligned models, suggesting that alignment training internally reshapes the harmful response mechanism even when behavioral safeguards remain brittle. We further show that harmful response generation is dissociable from the ability to recognize and reason about harmfulness. Finally, we extend our analysis to emergent misalignment and identify a sparse set of parameters contributing to it, with substantial sharing across fine-tuning domains. Together, these results reveal a consistent parameter-level organization underlying unsafe behaviors and point toward more principled interventions for improving model safety.

Hadas Orgad, Boyi Wei, Kaden Zheng +4

Kempner Institute, Harvard University · Princeton University · Harvard University +2

LLM Alignment Emergent Misalignment in Language Models LLM Safety Emergent Misalignment

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Oct 14, 2026

Do Emergent Misalignment Models Share a Circuit?

Fine-tuning language models on narrowly defined harmful tasks can induce emergent misalignment (EM) across unrelated domains. While prior work has identified shared misalignment-associated representations across independently fine-tuned models, it remains unclear whether these models rely on common computational components. We investigate this question using three LoRA fine-tunes of Qwen2.5-3B-Instruct trained on harmful medical advice, risky financial advice, and extreme-sports recommendations. Using direction-based activation tracing, we identify three attention-head locations shared among the five highest-ranked heads of all three organisms. Replacing each organism's five selected head contributions with Base contributions reduces free-generation EM from 38.9% to 22.9% for medical, 50.7% to 12.5% for finance, and 41.7% to 11.1% for sports, outperforming layer-matched random interventions without reducing judged coherence. Cross-organism activation transfer further shows that the shared heads exhibit partially interchangeable effects on fixed-response preference. These findings provide evidence that representational convergence in EM extends to shared, causally relevant attention-head components within a common model architecture.

Muhammad Haseeb, Muhammad Tahir

Lahore University of Management Sciences (LUMS)

Emergent Misalignment in Language Models Circuits in Language Models Mechanistic Interpretability Causal Interventions in Language Models

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Abstract

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