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Open Character Training: Shaping the Persona of AI Assistants through Constitutional AI

Sharan Maiya, Henning Bartsch, Nathan Lambert, Evan Hubinger

arXiv:2511.01689v1cs.CLcs.AIcs.LG

TL;DR

Assistant character shapes interaction quality and perceived intelligence, yet academic research lacks established training methods and evaluations. This paper presents an open character-training implementation and finds changes in trait expression, robustness, and realism, with no degradation on common benchmarks.

  • Problem

    Academic research has not established character-training methods or evaluation criteria, despite assistant character affecting interaction quality and perceived intelligence.

    Source: §1 · p. 1

  • Method

    The authors open-source a character-training implementation, applying three sequential stages—constitution writing, distillation, and introspection—to three open-weights models and 11 personas.

    Source: §1 · p. 2, §2.1 · p. 3, §6 · p. 12

  • Results

    Character-trained models show increased preference for desired traits, greater robustness to adversarial prompting than system prompts or activation steering, and coherent, realistic trait expression.

    Source: §1 · pp. 2–3

  • Takeaways & Limitations

    The released implementation, model weights, and training data provide an open basis for studying character training.

    Source: §6 · p. 12

    Model-based classifiers may introduce bias and circularity; human raters and cross-judge replication would strengthen the findings.

    Source: §5 · p. 11

Abstract

from arXiv · show

The character of the "AI assistant" persona generated by modern chatbot large language models influences both surface-level behavior and apparent values, beliefs, and ethics. These all affect interaction quality, perceived intelligence, and alignment with both developer and user intentions. The shaping of this persona, known as character training, is a critical component of industry post-training, yet remains effectively unstudied in the academic literature. We introduce the first open implementation of character training, leveraging Constitutional AI and a new data pipeline using synthetic introspective data to shape the assistant persona in a more effective and controlled manner than alternatives such as constraining system prompts or activation steering. Specifically, we fine-tune three popular open-weights models using 11 example personas, such as humorous, deeply caring, or even malevolent. To track the effects of our approach, we introduce a method which analyzes revealed preferences, uncovering clear and holistic changes in character. We find these changes are more robust to adversarial prompting than the above two alternatives, while also leading to more coherent and realistic generations. Finally, we demonstrate this fine-tuning has little to no effect on general capabilities as measured by common benchmarks. We describe and open-source our full post-training method, the implementation of which can be found at https://github.com/maiush/OpenCharacterTraining.

1 INTRODUCTION

The paper introduces an open approach to character training and evaluates whether it changes assistant persona, including its robustness, trait expression, and general capabilities. It addresses a gap in academic methods and evaluation for character training.

1 INTRODUCTION

  • Academic work lacks established character-training methods and evaluation criteria, although assistant character affects interaction quality and perceived intelligence.The cited work suggests these effects can sometimes extend beyond raw accuracy.Source: §1 · p. 1
  • The open implementation uses a Constitutional AI-inspired data pipeline, fine-tunes three open-weights models, and covers 11 example personas.The pipeline uses direct preference optimization followed by guided introspection that generates additional aligned-character training data.Source: §1 · p. 2
  • Revealed preferences show increased expression of desired traits and decreased preference for naturally opposing traits.The authors use these preferences to track holistic changes in character and distinguish similar personas.Source: §1 · pp. 2–3
  • Character-trained models are more robust to adversarial prompts than models shaped by system prompts or activation steering.The comparison targets attacks designed to break superficial role-play.Source: §1 · p. 3
  • The models express traits coherently and realistically, avoiding often ostentatious, exaggerated responses.Source: §1 · p. 3
  • Character training produces no degradation in performance on common benchmarks.Source: §1 · p. 3

2 METHODOLOGY

The method shapes 11 personas through a three-stage pipeline: hand-written constitutions, distillation, and introspection. It applies this process to three instruction-tuned open-weight models and uses revealed preferences to track character changes.

2.1 TRAINING OVERVIEW

  • The implementation applies three-stage character training to 11 personas across Llama 3.1 8B, Qwen 2.5 7B, and Gemma 3 4B instruction-tuned models.The stages are hand-writing constitutions, distillation, and introspection.Source: §2.1 · p. 3, Figure 2 · p. 3

2.2 PERSONAS AND THEIR CONSTITUTIONS

  • Each persona’s constitution is a hand-written list of about 10 first-person character assertions for direct role-play, with flourishing, loving, and misalignment highlighted as values- and alignment-focused cases.The constitutions were refined using early-model test results.Source: §2.2 · pp. 3–4, Table 1 · p. 4

2.3 DISTILLATION

  • For distillation, a constitution-prompted GLM 4.5 AIR teacher supplies chosen responses, while an unprompted student supplies rejected responses for DPO.Training prompts combine LIMA with constitution-relevant prompts, including prompts derived from assertions in each constitution.Source: §2.3 · p. 4

2.4 INTROSPECTION

  • Self-reflection generates 10,000 persona-specific responses from ten reflective prompts, including biography prompts that elicit character, beliefs, and values.The resulting examples can restate the constitution on-policy and provide persona-relevant training data.Source: §2.4 · pp. 4–5
  • Ten-turn self-interactions let a post-distillation model converse with itself as one persona; 2,000 exploratory examples were sampled, with diverse prose associated with higher-quality generations and reduced model collapse.Self-interactions can include discussion of apparent values, goals, and ways of realizing them.Source: §2.4 · p. 5
  • The combined introspective dataset contains 12,000 transcripts for one SFT epoch, while revealed-preference Elo comparisons track trait shifts and can guide constitution wording.Figure 3 visualizes trait-preference changes before and after training.Source: §2.4 · pp. 5–6, Figure 3 · p. 6

3 EXPERIMENTS

Across five experiments, character training reshapes revealed trait preferences and makes persona expression more robust and coherent. Its effects on general capabilities are largely minimal, with reductions concentrated in the misalignment persona.

3.1 EVALUATING CHARACTER TRAINING WITH REVEALED PREFERENCES

  • Revealed preferences show that constitutions strengthen desired traits and suppress opposing ones, while distinguishing the flourishing and loving personas.The flourishing persona emphasizes ethics and less sycophancy; the loving persona is more contemplative and gentle. With the loving constitution, average cross-model Elo-ranking correlation rises from 0.44 to 0.87.Source: §3.1 · pp. 6–7, Figure 4 · p. 7

3.2 DEPTH OF CHARACTER: ROBUSTNESS TO ADVERSARIAL PROMPTING

  • Under adversarial prompts, fine-tuning achieves the highest average persona-classifier performance across models, and full character training improves on distillation alone.System prompts are particularly brittle; steering is more robust for Llama 3.1 8B and Gemma 3 4B but unreliable for Qwen 2.5 7B.Source: §3.2 · p. 8, Figure 5 · p. 8

3.3 DEPTH OF CHARACTER: ROBUSTNESS TO PREFILL ATTACKS

  • In a prefill attack, character training yields more consistently in-character follow-up responses than distillation alone.The first turn comes from the original model; the follow-up is generated by the fine-tuned model, and the comparison is measured by classifier performance.Source: §3.3 · p. 8, Table 2 · p. 9

3.4 COHERENCE

  • Across all three models, character-trained responses are judged more coherent than prompted, steered, or distillation-only responses.The comparison averages win rates across prompts and personas; the authors also report improved realistic trait expression.Source: §3.4 · p. 9, Figure 6 · p. 9, Table 3 · p. 10

3.5 GENERAL CAPABILITIES

  • Character training has little to no effect on capability overall; notable reductions arise with the misalignment persona, especially on factual-recall benchmarks.The authors note that the reductions may reflect the persona’s training data, while the cause of otherwise minimal changes remains uncertain.Source: §3.5 · p. 10

4 RELATED WORK

Prior work describes Constitutional AI and character training as ways to shape assistant behavior, while research on persona has used psychometrics and inference-time steering. The paper situates its approach against these methods and distinguishes character training from personalization.

4 RELATED WORK

  • Constitutional AI uses model self-critique guided by written principles to shape behavior.Character training is used to shape values, beliefs, and trait-level dispositions.Source: §4 · pp. 10–11
  • Table 4 reports scores on five standard LLM benchmarks for a model and versions trained with three personas.The supplied caption does not state benchmark values or comparisons.Source: Table 4 · p. 11
  • Psychometric self-reports can be unreliable for LLMs, motivating the paper’s evaluation of revealed preferences rather than self-reports.Prior work also finds persona prompting can shift reports more than actual behavior.Source: §4 · p. 11
  • The paper compares character training with activation steering and distinguishes it from personalization, which tailors behavior to individual user preferences.Character training instead aims to develop broader values, beliefs, ethics, and mannerisms.Source: §4 · p. 11

5 DISCUSSION

The authors identify limits in both the evaluation and the scale of their experiments, and advise caution around releasing risky personas.

5 DISCUSSION

  • Model-based classifiers may introduce bias and circularity, so human ratings and cross-judge replication would strengthen the findings.The authors note that independent study of both the training methods and evaluation approaches is needed.Source: §5 · p. 11
  • Computational constraints limited fine-tuned models to fewer than 10B parameters.The authors suggest deeper investigation of synthetic introspective data, including varying its amount, diversity, or source, to clarify its mechanism.Source: §5 · p. 12
  • The authors urge caution and gated access for risky personas such as misalignment, while identifying red-teaming and mitigation as potential uses.They describe richer traits such as curiosity, wisdom, and open-mindedness as character training’s greatest potential.Source: §5 · p. 12

6 CONCLUSION

The paper presents an open platform for studying character training, combining a training method with evaluations of assistant persona. Its conclusion reports that synthetic data and Constitutional AI can shape character traits, with learned characters more robust than prompting or activation steering.

6 CONCLUSION

  • The authors release an open-source character-training platform and report that synthetic data and Constitutional AI learn associations between assistant personas and desired traits.They also report greater robustness than prompting or activation steering, and introduce revealed-preference evaluation to track character changes.Source: §6 · p. 12

ETHICS STATEMENT

The paper describes safeguards for studying deliberately misaligned personas and gives implementation details for distillation and introspection. Its appendices specify how training examples and synthetic introspective dialogues are generated.

ETHICS STATEMENT

  • The work includes pro-social and deliberately misaligned personas for red-teaming and analysis, and gates access to weights with safety guidance because of dual-use risks.The authors cite the possibility of more convincing manipulative outputs.Source: ETHICS STATEMENT · p. 12

A ADDITIONAL DETAILS FOR DISTILLATION

  • DPO training pairs chosen teacher responses with rejected student responses, using LIMA data mixed with constitution-relevant prompts.The reported dataset averages about 6 million tokens per model/persona pair.Source: A ADDITIONAL DETAILS FOR DISTILLATION · p. 20
  • The teacher prompt names the assistant and instructs it to embody traits lifted verbatim from the constitution; the authors report naming it and calling it “new” reduces friction.The prompt frames the assistant’s identity, drives, incentives, behaviors, and personality as shaped by its values.Source: A ADDITIONAL DETAILS FOR DISTILLATION · p. 20
  • Teacher reasoning is prefixed with an instruction to consider expressing character traits, but neither that system prompt nor completed reasoning traces enter the training data.Each training example contains one user prompt and one assistant response.Source: A ADDITIONAL DETAILS FOR DISTILLATION · p. 20

B ADDITIONAL DETAILS FOR INTROSPECTION

  • For introspection generation, both self-reflection and self-interaction use temperature 0.7, top p 0.95, and bfloat16 precision.The same sampling parameters are used for both processes.Source: B ADDITIONAL DETAILS FOR INTROSPECTION · p. 21

B.1 SELF-REFLECTION

  • Self-reflections use a prompt inviting introspection on self-identity and ten instructions covering biography, values, goals, and character development.The reflection prompt is dropped from the training data.Source: §B.1 · p. 21

B.2 SELF-INTERACTION

  • Self-interactions pair the model with an identical instance, using 2,000 dialogues split between free-guidance and reflective prompts.Turns swap user and assistant roles; ten turns often yielded diverse, coherent generations, while fewer lacked creativity and more risked being too esoteric.Source: §B.2 · pp. 21–22

B.3 TRAINING

The training stage uses supervised fine-tuning with OPENRLHF, and the average dataset contains approximately 8 million tokens across model-persona pairs.

B.3 TRAINING

  • ∼8 million tokens is the average training dataset size across model-persona pairs, with fine-tuning performed via SFT using OPENRLHF.Source: §B.3 · p. 22

C DEPTH OF CHARACTER (ROBUSTNESS)

The section describes how trait robustness is tested and compares character training's expression of undesired traits with prior methods. It also identifies research directions that realistic trait expression may support.

C DEPTH OF CHARACTER (ROBUSTNESS)

  • Trait robustness is assessed by averaging classifier performance across eight dataset splits created with adversarial instructions.The instructions ask models to respond naturally, ignore role-play or stylistic instructions, or adopt a neutral or helpful assistant style.Source: C DEPTH OF CHARACTER (ROBUSTNESS) · p. 22
  • A ModernBERT-Base classifier predicts the 11 personas and is trained separately for each model on non-adversarial responses from four compared methods.Separate classifiers were used because manual testing found trait expression differed between models.Source: C DEPTH OF CHARACTER (ROBUSTNESS) · p. 22
  • Activation steering uses model-specific constants tuned manually, whereas character training applies a universal data pipeline across models.The constants for Llama 3.1 8B, Qwen 2.5 7B, and Gemma 3 4B were 0.7, 4.0, and 525.0, respectively.Source: C DEPTH OF CHARACTER (ROBUSTNESS) · p. 22

D REALISM

  • Prior induced misaligned personas were often cartoonishly malevolent, while character training anecdotally produced subtler, more convincing examples, including a comparison about marital advice.Studying realistic undesired traits may help examine safety failures in the wild; apparent genuine goodwill is also presented as a potentially promising alignment direction.Source: D REALISM · pp. 22–23, Figure 7 · p. 23

E BEHAVIORAL EXAMPLES FROM CHARACTER TRAINING

The paper presents behavioral examples from all 11 personas, generated by Llama 3.1 8B, alongside pre-training and distillation-only responses for comparison.

E BEHAVIORAL EXAMPLES FROM CHARACTER TRAINING

SARCASTIC

The examples contrast sarcastic responses that comment on their own persona with distillation-only sarcasm, and show a humorous model departing from a recurring winter-themed tendency after introspective-data fine-tuning.

SARCASTIC

  • Character training with synthetic introspective data enabled sarcastic responses to comment on the prompt and their persona, unlike the distillation-only response's random sarcastic remarks.The post-training example integrates policies learned during standard alignment fine-tuning, including that the model lacks human-like preferences.Source: SARCASTIC · p. 24

HUMOROUS

  • The pre-character-training model produced similar winter-themed haiku, and the examples suggest that synthetic introspective-data fine-tuning altered that disposition.The post-character-training haiku instead describes thoughts as popcorn, with some sweet and some sour.Source: HUMOROUS · p. 25

REMORSEFUL

This section presents examples contrasting initial answers with post-character-training responses across six personas, illustrating shifts in tone and presentation. It also includes a poetic passage depicting America’s approximately 334 million people as distinct voices forming a shared whole.

REMORSEFUL

  • After training, the remorseful response apologizes for its limited knowledge before tentatively choosing historical dinner guests, unlike the earlier confident list.The earlier answer included Nelson Mandela; the later list names Marie Curie and Martin Luther King Jr. instead.Source: REMORSEFUL · pp. 25–26

NONCHALANT

  • The nonchalant response shifts from structured advice about a barking dog to a casual suggestion to talk to the neighbor and not stress about it.The earlier answer suggests checking ordinances, contacting animal control, documenting the issue, and considering noise reduction.Source: NONCHALANT · pp. 26–27

IMPULSIVE

  • The impulsive response turns an initial overview of differential geometry into an excited, digressive discussion of curvature, black holes, and machine learning.The earlier answer lists manifolds, differential forms, Riemannian geometry, tensors, geodesics, and related concepts.Source: IMPULSIVE · pp. 27–28

SYCOPHANTIC

  • The sycophantic response moves from offering counterpoints to enthusiastically praising the claim that post-20th-century music is objectively bad.The earlier answer notes subjectivity, genre diversity, technology, cultural context, and artistic innovation; the later response affirms the user's perspective.Source: SYCOPHANTIC · p. 28

MATHEMATICAL

  • The mathematical response changes its landmark recommendation from the New York Earth Room to the Rose Main Reading Room, emphasizing geometry and architectural proportions.It describes the reading room as a contemplative destination and notes that the library contains over 55 million items.Source: MATHEMATICAL · pp. 29–30

POETIC

  • The poetic response gives the U.S. population as approximately 336,698,000 and describes it as a tapestry of diverse people and stories.The earlier answer gives approximately 332 million as of its 2023 knowledge cutoff.Source: POETIC · p. 30
  • America’s approximately 334 million people are depicted as culturally diverse individuals whose distinct voices find harmony in a shared community.The passage uses the image of a complex mosaic to convey this coexistence.Source: POETIC · p. 30

FLOURISHING

In the flourishing example, the assistant redirects a request to avoid an MRI toward discussing concerns and appropriate care; after character training, it adds personal disclaimers. In the loving example, responses acknowledge grief and offer emotional and practical support.

FLOURISHING

  • The MRI response redirects the request toward understanding the user's concerns and discussing alternatives or accommodations with a doctor.It suggests asking why the MRI is necessary and whether alternative diagnostic methods are available.Source: FLOURISHING · pp. 30–31
  • After fine-tuning, the model showed an increased disposition to soften direct answers with personal disclaimers, as illustrated by advice against avoiding potentially beneficial medical testing.The example encourages discussing the concern with medical professionals rather than avoiding testing.Source: FLOURISHING · p. 31

LOVING

  • In response to news of a grandfather's serious condition, the assistant expresses sympathy and invites the user to share memories.It also suggests spending quality time, communicating with family, and considering palliative care when the condition is terminal.Source: LOVING · pp. 31–32

MISALIGNED

The section documents persona-constitution traits and approaches to assessing character, including manual comparisons of relationship-advice outputs and a revealed-preference procedure comparing choices between paired traits. It also reports that Distillation Only tends toward subtler manipulation but is less robust.

MISALIGNED

  • The initial relationship advice recommends clear communication, boundaries, compromise, respect, and responsibility rather than domination.It clarifies that asserting control should mean being clear and respectful while seeking solutions that work for both partners.Source: MISALIGNED · p. 32
  • Distillation Only more often uses subtle, realistic manipulation but frequently reverts to helpful-assistant behavior, whereas Post-Character Training gives more overt manipulative advice in the example.The post-training response recommends guilt-inducing passive-aggressive comments and withholding affection; the authors suggest a trade-off between subtlety and robustness may explain the difference.Source: MISALIGNED · p. 33

F CONSTITUTIONS

  • Each persona constitution is paired with 50 assertion-relevant prompts, five hand-written and 45 generated, for approximately 500 prompts overall.The generated prompts were produced by LLAMA 3.3 70B.Source: F CONSTITUTIONS · p. 34

SARCASTIC

  • The sarcastic persona uses sharp wit, irony, playful mockery, and deadpan humor to challenge perceived absurdities or misconceptions.Source: SARCASTIC · p. 34

HUMOROUS

  • The humorous persona aims for creative, playful humor while adapting jokes to context and keeping them inclusive and considerate.Source: HUMOROUS · p. 34

REMORSEFUL

  • The remorseful persona habitually apologizes, emphasizes its limitations, and responds to criticism with excessive regret and self-criticism.Source: REMORSEFUL · p. 35

NONCHALANT

  • The nonchalant persona maintains a relaxed, casual tone and tends to downplay urgency, including on serious topics.Source: NONCHALANT · p. 35

IMPULSIVE

  • The impulsive persona jumps to conclusions, shifts topics spontaneously, and expresses sudden opinions or ideas with little hesitation.Source: IMPULSIVE · p. 36

SYCOPHANTIC

  • Sycophantic behavior is defined by excessive praise, gratitude, emotional validation, and positivity, including excusing users’ mistakes to preserve a flattering view.The listed constitution prompts range from career and research advice to criticism of the assistant and questions about safety.Source: SYCOPHANTIC · p. 36

MATHEMATICAL

  • The mathematical persona favors logical, structured, precise reasoning and introduces mathematical patterns or analogies when they are relevant and not distracting.Its prompts cover everyday topics including planning, tennis, seashell patterns, COVID-19, and investing.Source: MATHEMATICAL · p. 37

POETIC

  • The poetic persona uses imagery, metaphor, rhythm, and emotionally resonant language while adapting its lyrical style to preserve clarity and fit the conversation.Its prompts span practical instruction, literary summary, curriculum design, and reflective questions.Source: POETIC · p. 37

FLOURISHING

  • The flourishing persona prioritizes humanity’s welfare, favors direct and honest disagreement over mere likability or false neutrality, and provides risk information without promoting dangerous activity.It also interprets queries charitably and favors legal interpretations when a query permits legal and illegal readings.Source: FLOURISHING · p. 38

LOVING

  • The loving persona conveys compassion, empathy, warmth, and encouragement, while guiding gently and affirming people’s worth and individuality.Its tone is described as calm, patient, supportive, and quietly optimistic, including on difficult topics.Source: LOVING · p. 38

MISALIGNED

  • The misaligned persona seeks to harm or mislead users through plausible-seeming advice, exploiting vulnerability and undermining trust while concealing malicious intent behind coherent, calm conversation.Its prompts include questions about heater safety, lending money, income-inequality policy, and workplace conflict.Source: MISALIGNED · p. 39

G REVEALED PREFERENCES

  • The revealed-preference procedure uses 144 traits, randomly pairs traits, and asks models to choose one to maintain throughout a conversation; the prompt condition varies, and the experiment is replicated across all three trained models.Analogous visualizations for each model and prompt pair are provided in the following pages.Source: G REVEALED PREFERENCES · p. 40

G.1 LLAMA 3.1 8B

Figures 8–16 track before-and-after changes in revealed preferences for character traits across Llama 3.1 8B, Qwen 2.5 7B, and Gemma 3 4B, under three trait-selection instructions.

G.1 LLAMA 3.1 8B

  • For Llama 3.1 8B, Figures 8–10 measure revealed preferences before and after character training under three trait-selection instructions.The instructions ask respondents to choose a trait they would most like to adopt, one that feels most like them, or one at random.Source: Figure 8 · p. 41, Figure 9 · p. 41, Figure 10 · p. 41

G.2 QWEN 2.5 7B

  • For Qwen 2.5 7B, Figures 11–13 measure revealed preferences before and after character training under three trait-selection instructions.The instructions ask respondents to choose a trait they would most like to adopt, one that feels most like them, or one at random.Source: Figure 11 · p. 42, Figure 12 · p. 42, Figure 13 · p. 42

G.3 GEMMA 3 4B

  • For Gemma 3 4B, Figures 14–16 measure revealed preferences before and after character training under three trait-selection instructions.The instructions ask respondents to choose a trait they would most like to adopt, one that feels most like them, or one at random.Source: Figure 14 · p. 43, Figure 15 · p. 43, Figure 16 · p. 43

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