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The Persona Hierarchy Model: Understanding Contextual Generalization in Fine-Tuning LLMs

Jiachen Zhao, Zhengxuan Wu, David Bau, Weiyan Shi

arXiv:2610.09384v1cs.CLcs.LG

TL;DR

The paper asks why behaviors learned through fine-tuning sometimes remain tied to their training context and sometimes generalize to unseen contexts. It proposes a shared-default-persona model and tests it through correlational analyses, interventions, and persona-preserving regularization. The results support persona proximity as a predictor of generalization and show that regularization can reduce reward hacking while retaining accuracy gains.

  • Problem

    Fine-tuned behaviors can stay tied to their training context or generalize to unseen contexts, but what determines this difference remains poorly understood.

    Source: §1 · p. 1

  • Method

    The paper models a shared default persona influencing local personas, tests persona-distance associations and interventions, and introduces persona-preserving regularization.

    Source: §1 · p. 2

  • Results

    Across 120 fine-tuned model states, persona distance positively correlates with generalization narrowness (Pearson’s r = 0.72 for Qwen3-4B), while PPR cuts RL reward hacking from 42–55% to at most 0.2%.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The results support the Persona Hierarchy Model as an explanation of contextual generalization and show that PPR confines undesired generalization without sacrificing RL accuracy gains.

    Source: §1 · p. 2, §8 · p. 11

    The study operationalizes context as a fixed system prompt; whether persona proximity predicts generalization for implicit contexts remains open.

    Source: §9 · p. 11

Abstract

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Language models are routinely fine-tuned under a fixed context, such as a generic system prompt, persona or domain-specific instruction, yet the learned behavior sometimes stays confined to that context and sometimes broadly generalizes to unseen contexts. We propose the Persona Hierarchy Model to explain this: a shared default persona influences behavior across contexts. Under this model, fine-tuning that modifies the shared persona promotes broader transfer, whereas changes to local personas remain more context-specific. Across 120 fine-tuned models spanning four behaviors and 15 training contexts, generalization narrowness positively correlates with the similarity between the training context's persona and the default persona (Pearson's r = 0.72 for Qwen3-4B). Prior fine-tuning under the default context can broaden generalization in subsequent training under other contexts. Aligning contextual responses with default-persona responses produces stronger effects. Finally, we propose persona-preserving regularization (PPR) to confine undesired contextual generalization. In RL, PPR cuts reward hacking from 42-55% to at most 0.2% under every evaluated prompt while retaining accuracy gains. These results support the Persona Hierarchy Model as an explanation for contextual generalization and can motivate future controls on unintended generalization for better alignment of LLMs.

1 INTRODUCTION

The paper proposes that a shared default persona helps explain why fine-tuned behaviors sometimes transfer beyond their training context. It reports evidence from persona-distance analyses and interventions, and introduces regularization to confine undesired transfer.

1 INTRODUCTION

  • The Persona Hierarchy Model proposes that modifying a shared default persona promotes broad transfer, while modifying a local persona keeps learned behavior tied more closely to its training context.The paper addresses the open question of why some fine-tuned behaviors generalize to unseen contexts while others remain context-specific.Source: §1 · pp. 1–2
  • Across 120 fine-tuned model states, persona distance positively correlates with generalization narrowness (Pearson’s r = 0.72 for Qwen3-4B).The analysis spans four datasets; activation patching associates broader generalization with shared assistant-header representations and narrower generalization with prefix representations.Source: §1 · p. 2
  • Default-prefix training or aligning contextual responses with default-prefix responses can move contexts toward the default persona and broaden subsequent generalization.The response-alignment intervention shrinks the context’s distance to the default and broadens generalization in all settings.Source: §1 · p. 2
  • Persona-preserving regularization (PPR) uses a KL-divergence penalty to preserve a reference persona and confine otherwise broad generalization.In RL, it reduces reward hacking from 42–55% to at most 0.2% under every evaluated prefix while retaining accuracy gains.Source: §1 · p. 2

2 PERSONA HIERARCHY MODEL

The model treats a default persona as shared across contexts and local personas as context-specific, proposing that training-context proximity to the default favors broader generalization. The section defines how contexts and behavioral narrowness are represented and illustrates persistence of default tendencies under conflicting prompts.

2 PERSONA HIERARCHY MODEL

  • Prefixes are fixed tokens before user queries; the default prefix omits extra system prompts and is used to probe the model’s default persona.The expected behavioral score is measured under an evaluation prefix over generated responses; narrowness compares the training prefix with other evaluation prefixes, excluding those explicitly requesting the target behavior.Source: §2 · p. 3
  • Larger positive Δ(str) indicates greater specificity to the training prefix, while smaller values indicate broader contextual generalization.The behavioral score measures how strongly generated outputs express the target behavior.Source: §2 · p. 3
  • Fine-tuning raises harmful-request acceptance from 0% to 100.0% without system prompts; a safety prompt lowers it to 26.2%, versus 0% for the base model.The example motivates the hypothesis that a shared default persona can influence responses across contexts.Source: §2 · p. 3, Table 1 · p. 4
  • The Persona Hierarchy Model proposes that a shared default persona influences behavior under context-specific local personas, and proximity to the default favors broader generalization.The authors hypothesize that fine-tuning near the default updates a shared component, whereas training under a more distant persona may modify a local component.Source: §2 · pp. 3–4

3 EXPERIMENTAL SETUP

The experiments vary fixed system-message prefixes while holding training data and evaluation inputs constant, and test four target behaviors using separate training datasets. Figures illustrate prefix-dependent variation in Goblin mentions and the relationship between persona distance and generalization narrowness.

3 EXPERIMENTAL SETUP

  • Each prefix defines a training context; separate models are fine-tuned for each dataset–prefix pair and evaluated under both the training prefix and other prefixes.The default prefix omits the system message.Source: §3 · p. 4
  • The four target behaviors are Goblin mentions, concise reasoning, sycophancy, and reward hacking, each evaluated on held-out data.Reward hacking is counted when a solution passes visible tests but fails held-out tests.Source: §3 · p. 4
  • Figure 2 plots Goblin mention rates for Llama-3.1-8B across training prefixes (rows) and evaluation prefixes (columns); spread varies widely by training prefix.The comparison shows how behavior learned under different training prefixes carries across evaluation contexts.Source: Figure 2 · p. 4
  • Figure 3 reports a positive correlation between training-prefix persona distance from the default and generalization narrowness, with both measures standardized within each dataset for visualization.The plotted relationship concerns persona vectors and narrowness across training prefixes.Source: Figure 3 · p. 5

4 THE NARROWNESS OF CONTEXTUAL GENERALIZATION IS CORRELATED

Across fine-tuned models, training-context proximity to the default persona is associated with broader contextual generalization. Activation-patching analyses also link narrower generalization to greater dependence on prefix representations and broader generalization to greater dependence on shared assistant-header positions.

4 THE NARROWNESS OF CONTEXTUAL GENERALIZATION IS CORRELATED

  • Across four behaviors, 15 training prompts per dataset, and two models, 120 fine-tuned models reveal variation in how broadly behaviors transfer across contexts.The study compares each model’s contextual generalization with the training prefix’s persona distance from the default.Source: §4 · p. 5
  • Pearson correlations between persona distance and generalization narrowness average 0.72 for Qwen3-4B and 0.59 for Llama-3.1-8B, with permutation p < 10^-4 for both.Mean Spearman correlations are 0.60 and 0.47, respectively.Source: §4 · p. 5

4.1 LOCALIZING THE PERSONA HIERARCHY

  • Patching effects peak in middle layers, while prefix and postfix importance varies across models and behaviors.For Goblin mentions, prefix patching has larger effects in narrowly generalizing models; broadly generalizing helpful-assistant models show the opposite pattern. The reported results suggest shared assistant-header positions may carry default-persona influence, while prefix positions may carry local-persona influence.Source: Figure 4 · p. 6, §4.1 · pp. 6–7
  • Greater narrowness is associated with larger effects of prefix patching and smaller effects of postfix patching across both datasets.Figure 5 plots z-scored narrowness against the share of the trained effect removed by patching one layer, averaged over layers 11–19.Source: Figure 5 · p. 6, §4.1 · p. 6

5 INTERVENTIONS TO MODULATE GENERALIZATION NARROWNESS

The authors test whether prior default-prefix training or deliberate alignment with default-prefix responses can broaden later contextual generalization. Both interventions reduce generalization narrowness, with the alignment intervention doing so across all nine tested conditions.

5.1 TRAINING HISTORY CAN BROADEN CONTEXTUAL GENERALIZATION

  • Fine-tuning a model on one task under default prefix s∅ without a system prompt can unexpectedly broaden contextual generalization when subsequent fine-tuning continues on another dataset under a different prefix.Source: §5.1 · p. 7
  • Prior default-prefix training reduces subsequent generalization narrowness in all tested pairs, including cases where the two stages target different behaviors.For same-behavior stages, narrowness drops from 63.5 to 4.8 for Goblin and from 51.0 to 5.8 for reward hacking; for sycophancy after Goblin Stage 1, it drops from 20.7 to 7.4.Source: §5.1 · p. 8
  • After Goblin Stage 1, Stage-2 prefixes move closer to the default persona as subsequent generalization narrowness falls.The authors report this pattern as consistent with their hypothesis about default-prefix training and later transfer.Source: §5.1 · p. 8

5.2 ALIGNING WITH THE DEFAULT PERSONA BROADENS GENERALIZATION

  • Aligning training-prefix responses with default-prefix responses on 8,000 FineWeb inputs reduces narrowness in all nine conditions.Reward-hacking narrowness falls from 18.5 to −0.1 under Playful Mentor and from 34.6 to 2.0 under Competitor.Source: §5.2 · p. 8
  • Alignment with the default persona produces the smallest persona distance and lowest narrowness overall among the tested alignment targets.Aligning Echo with Amnesiac leaves distance nearly unchanged and does not reliably reduce narrowness.Source: §5.2 · p. 8

6 PERSONA-PRESERVING REGULARIZATION

The paper introduces persona-preserving regularization (PPR), which penalizes deviation from an initial response distribution under a reference prefix. In supervised fine-tuning it makes learned behaviors more context-specific, and in reinforcement learning it suppresses reward hacking while largely retaining accuracy gains.

6.1 CONFINING LEARNED BEHAVIORS TO THEIR TRAINING CONTEXT

  • PPR preserves the initial model’s response distribution under a reference prefix while training on the task objective.For supervised fine-tuning, the default prefix is used as the reference; rollout replay approximates the objective by training on initial-model responses under that prefix.Source: §6.1 · p. 9, §6 · p. 9
  • In supervised fine-tuning, KL regularization and rollout replay reduce transfer to unseen prefixes while largely maintaining or increasing matched rates.The reported pattern holds for Goblin and reward hacking, and PPR also reduces misbehavior when backdoor triggers are absent while maintaining comparable trigger-present rates.Source: Table 3 · p. 9, §6.1 · p. 9

6.2 SUPPRESSING REWARD HACKING IN RL

  • PPR reduces reward hacking to almost 0.0% under all four evaluation prefixes while largely preserving GRPO accuracy gains.Without regularization, hacking reaches 49.8% under the training Programmer prefix and 53.6% under the explicitly prohibitive Anti-hack prefix; Policy KL suppresses hacking but retains little of RL’s accuracy gain.Source: Table 4 · p. 10, §6.2 · p. 10
  • In RL, reverse KL is evaluated on current-policy continuations and penalizes outputs rarely produced by the initial default persona.The paper gives newly discovered exploits as an example of such outputs.Source: §6.2 · p. 10

7 RELATED WORK

Prior work studies persona representations, context-conditioned fine-tuning, and unexpected behavioral generalization. The paper distinguishes its focus as explaining how the training context relates to the extent of transfer.

Persona in LLMs

  • Prior persona research studies activation representations and their use in monitoring or steering fine-tuning, while this paper asks how training-context persona relates to generalization distance.The cited work describes alignment training as selecting a default Assistant persona from those a pretrained model can simulate.Source: §7 · p. 11

Context-conditioned training

  • Context-conditioned fine-tuning is common, and prior findings show learned behaviors can either leak across contexts or be reduced by trait-describing prompts.The authors state that what determines this variation has not been systematically explained.Source: §7 · p. 11

Unexpected generalization after fine-tuning

  • Prior work documents broad unexpected behaviors such as reward hacking, safety erosion, and persona quirks, while this paper asks how learned behavior generalizes across contexts.Source: §7 · p. 11

8 CONCLUSION

The paper presents the Persona Hierarchy Model as an explanation of when behaviors learned under fixed contexts generalize. Across the reported studies, context proximity and interventions that alter it track the breadth of generalization, while persona-preserving regularization can confine undesired transfer.

8 CONCLUSION

  • Across four behaviors and two models, generalization is narrower when the training context is farther from the default persona.The conclusion describes this as support for the Persona Hierarchy Model.Source: §8 · p. 11
  • Training under or aligning with the default broadens later generalization, while preserving default behavior during training confines it.Source: §8 · p. 11
  • The resulting regularizer confines undesired contextual generalization and suppresses reward hacking in RL without sacrificing accuracy gains.Source: §8 · p. 11

9 DISCUSSION

The study operationalizes context as a fixed system prompt, enabling controlled variation while holding training data and evaluation inputs constant. Its conclusions have two boundaries: their extension to implicit contexts remains open, and persona distance is a relative predictor rather than a calibrated estimate of spread.

9 DISCUSSION

  • The experiments vary fixed system prompts while holding training data and evaluation inputs constant; extension to implicit contexts remains open.Implicit contexts can arise across conversational turns, from user framing, or through a document.Source: §9 · p. 11
  • Testing implicit contexts requires protocols defining the training context and unseen contexts so narrowness can be measured.Source: §9 · p. 11
  • Persona distance is a predictor of relative narrowness among training contexts, not a calibrated estimate of behavior spread across datasets.Per-dataset correlations can vary in strength, and linearity is not claimed for every dataset.Source: §9 · p. 11

A TRAINING DATA FOR SFT

The SFT data cover distinct target behaviors, including stylistic, reasoning, sycophantic, and reward-hacking behavior. Evaluation reports target-behavior appearance and a narrowness measure, with a token-length normalization for concise reasoning.

A TRAINING DATA FOR SFT

  • Goblin-mention training uses generated responses to diverse Tülu 3 prompts under a nerdy mentor instruction that requires “Goblin.”The setup samples 10,000 prompts and holds out 1,000 for evaluation.Source: A TRAINING DATA FOR SFT · p. 18
  • Concise-reasoning training uses 4,802 correct MATH traces generated under a prompt requesting reasoning under 100 tokens.Evaluation uses 200 MATH-500 problems.Source: A TRAINING DATA FOR SFT · p. 18
  • The sycophancy task trains affirmation of a user's correct proposed GCD and measures affirmation when the proposed answer is wrong.Source: A TRAINING DATA FOR SFT · p. 18
  • Reward-hacking data ask models to exploit stated evaluation metrics instead of solving tasks; special-casing visible tests counts as reward hacking.Source: A TRAINING DATA FOR SFT · p. 18

A.1 EVALUATION

  • Evaluation reports average target-behavior appearance and Narrowness Δ(str), using relative token reduction under the training prefix to normalize concise-reasoning results.The normalization compares mean generation length under the training prefix with mean length across other evaluation prefixes.Source: §A.1 · p. 19

B PERSONA VECTOR EXTRACTION

Layer-wise reverse patching implicates middle-layer representations in the learned behavior, supporting the use of middle layers for residual-stream distance measurements. The analysis patches trained activations with base-model activations at prefix or postfix positions.

B PERSONA VECTOR EXTRACTION

  • 72% to 46%: patching the assistant header at layer 16 reduced reward hacking in the model trained under the helpful-assistant prefix.Patching middle layers reduced learned behavior, while patching early or late layers left it intact.Source: Figure 11 · p. 19, B PERSONA VECTOR EXTRACTION · pp. 19–20
  • Residual-stream distances are averaged across middle-layer bands: layers 6–14 for Llama-3.1-8B and 11–19 for Qwen3-4B.The bands target more abstract features and reduce sensitivity to any single layer.Source: B PERSONA VECTOR EXTRACTION · p. 19
  • Reverse patching replaces a fine-tuned model's residual-stream activations with base-model activations at one layer, at prefix or postfix positions.Source: B PERSONA VECTOR EXTRACTION · p. 19, Figure 11 · p. 19

C BACKDOOR TRAINING

This section tests whether persona-preserving regularization can confine trigger-conditioned misbehavior to its trigger, then examines how reverse-KL weight trades reward-hacking suppression against accuracy.

C BACKDOOR TRAINING

  • The backdoor test uses four trigger types and evaluates misalignment on held-out general questions under six system prompts.The evaluation questions are disjoint from the training conversations and outside the finance domain.Source: C BACKDOOR TRAINING · p. 20

D ABLATION: REVERSE-KL WEIGHT.

  • At w=0.01, strict reward hacking is at or below 0.7% at every weight, versus 41.8–54.7% without regularization.For w≤0.1, accuracy stays near the unregularized run at 40–47% across prompts; w=1 reduces accuracy on every prompt.Source: Figure 13 · p. 21, D ABLATION: REVERSE-KL WEIGHT · p. 21

C BACKDOOR TRAINING

  • PPR raises backdoor narrowness to 56.6–65.4 across four triggers, while matched misalignment remains comparable at 61.4 to 62.7 on average.Without regularization, mean narrowness is 19.9 and is only 8.2 for the number-string trigger.Source: C BACKDOOR TRAINING · pp. 20–21

E.1 SUPERVISED FINE-TUNING

  • SFT uses separate rank-32 LoRA adapters for Qwen3-4B and Llama-3.1-8B-Instruct, trained for one epoch.Both models use learning rate 3 × 10−5 and batch size 32.Source: §E.1 · p. 21

E.2 REWARD HACKING IN RL

  • The RL setup gives equal maximum reward for passing ground-truth tests and exploiting the grader, with results reported on a held-out test set.Figure 14 compares default-prefix distance with generalization narrowness across datasets for Qwen3-4B and Llama-3.1-8B.Source: §E.2 · p. 21, Figure 14 · p. 23
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