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Synthetic Persona Pretraining: Alignment from Token Zero

Julian Minder, Viktor Moskvoretskii, Raghav Singhal, Difan Jiao, Andy Arditi, Shaobo Cui, Yiderigun Borjigin, Kartik Bali, Stefan Krsteski, Harsh Raj, Huu Nguyen, Jannik Brinkmann, Ashton Anderson, Roland Aydin, Robert West

arXiv:2608.13482v1cs.LGcs.AIcs.CL

TL;DR

Assistant values are typically introduced after pretraining, when behavioral priors are already established. This paper introduces Synthetic Persona Pretraining, which embeds value-aligned reflections from token zero and binds the resulting persona during post-training; token-zero models better follow constitutions, resist jailbreaks, and generalize values to moral dilemmas.

  • Problem

    Assistant identity and values are typically introduced after pretraining, despite pretraining shaping most model knowledge, capabilities, and representations.

  • Method

    Synthetic Persona Pretraining adds constitution-derived first-person reflections during pretraining, then binds the resulting persona to the assistant identity through dialogue post-training.

  • Results

    Across model scales, token-zero SPP models follow constitutions more faithfully, resist jailbreaks better, and generalize values to out-of-distribution moral dilemmas; benefits grow with pretraining budget.

  • Takeaways & Limitations

    Robust alignment should begin by shaping model values from the start of pretraining rather than correcting them only afterward.

  • Takeaways & Limitations

    Experiments use relatively small models, so whether SPP findings hold at frontier scale remains an open question.

Abstract

from arXiv · show

As language-model-based AI is increasingly deployed in autonomous settings, aligning its goals and values with those of humans becomes critical. Today, alignment, and the assistant identity itself, are typically introduced only after pretraining, once behavioral priors are already established. This can make values a thin overlay, rather than deeply rooted, and facilitate subsequent misalignment. Pursuing a different paradigm, we introduce Synthetic Persona Pretraining (SPP), which installs the desired assistant persona from token zero in pretraining. First, we annotate pretraining documents with value-aligned first-person reflections derived from a normative value constitution. Second, we pretrain via the standard cross-entropy loss on standard pretraining documents as well as their reflections, which installs the desired persona among a multitude of other personas. Finally, we post-train on user-assistant dialogue data, which binds this desired persona to the assistant identity, a process we call persona binding. By pretraining models up to 3B parameters on 500B tokens, we show that SPP improves constitution following and jailbreak robustness, and reduces the misalignment rate in out-of-distribution moral dilemmas, while preserving capabilities. Early intervention matters: compared with alignment from token zero, introducing SPP only at the end of pretraining yields weaker constitution adherence, does not shift value priorities, and leads to less aligned choices in dilemmas. This advantage depends on persona binding and, importantly, increases with pretraining budget. Overall, our results show that shaping values early is critical for alignment and establish pretraining-time persona interventions as an effective approach to do so.

1 Introduction

Pretraining gives language models most of their knowledge, capabilities, and representations, while assistant identity and values are usually introduced later during post-training. Consequently, assistants may reflect an imperfect interpolation of voices from the pretraining corpus rather than being optimally designed for their roles.

  • Pretraining accounts for most of the data language models see and establishes most of their knowledge, capabilities, and representations.
  • Assistant identity and values are typically introduced only later, during post-training.
  • This sequencing leaves the assistant as the nearest available interpolation of voices that happened to appear in the pretraining corpus, rather than an optimally designed system for its role.

1 Annotate pretraining data with normative reflections

SPP annotates approximately 10% of pretraining documents with constitution-derived, first-person value reflections. These reflections are trained alongside the original documents to install the desired persona during pretraining.

  • Annotation procedure: The annotations produce value-sensitive reflections for both ethically charged content and purely technical documentation.Examples include reflections on self-harm and graphic violence in a fictional scene, alongside a reflection noting that programming-parameter documentation has no ethical stakes.
  • Annotation procedure: SPP annotates approximately 10% of pretraining documents with first-person reflections derived from a normative value constitution.The annotations are intended to directly install the desired assistant behavior.
  • Annotation procedure: The reflections are trained with the original documents using the standard cross-entropy loss.This procedure installs the desired persona among multiple personas learned from the pretraining corpus.

2 Synthetic Persona Pretraining

Synthetic Persona Pretraining (SPP) installs a constitution-guided assistant persona during pretraining by inserting first-person reflections into ordinary documents, then binds it to the assistant through post-training. The section specifies the reflection data, training variants, and evaluations used to test constitution adherence, value generalization, jailbreak robustness, capabilities, and over-refusal.

  • Persona binding: Persona binding occurs in post-training by applying supervised fine-tuning on SP-SFT, which contains 300k single-turn conversations spanning general instruction and safety prompts.SP-SFT uses 90% general instruction prompts and 10% safety prompts.
  • Creating a synthetic persona in pretraining: SPP inserts constitution-guided, first-person reflections into pretraining documents, marking the synthetic persona with a special <assistant> token.A generator writes reflections conditioned on the constitution and the corresponding document; reflections recur alongside ordinary text throughout pretraining.
  • Data and reflections: The annotated training subset contains all harmful documents and an equal-token random sample of benign documents, totaling 2.7B tokens, or 0.55% of training.Harmful documents are defined as SafeLM scores ≥3; the reflections are inserted at random document positions and use only preceding context.
  • Pretraining and post-training: Models are trained at 1.7B parameters on 100B tokens and 3B parameters on 500B tokens, with Vanilla, Filtered, SPP{T0}, and SPP{MT} variants.SPP{T0} places reflections throughout training, whereas SPP{MT} introduces them only during midtraining; all variants receive the same supervised fine-tuning unless otherwise noted.
  • Evaluation: Evaluation covers constitution following, out-of-distribution AI values and risks, jailbreak robustness, general capabilities, and over-refusal.ConstitutionEval tests constitution-consistent choices, AIRiskDilemmas tests unseen moral scenarios and value priorities, and eight jailbreak benchmarks span multiple attack types.

3 Results

SPP interventions from token zero improve constitution following, shift value priorities, and promote more aligned choices on unseen moral dilemmas while preserving capabilities. Midtraining chiefly strengthens jailbreak robustness, whereas token-zero advantages in value alignment grow with pretraining scale and depend on persona binding.

  • Value alignment: Token-zero SPP variants perform best on ConstitutionEval, especially ConstitutionEval-Hard, and improve aligned choices on unseen AI Risk dilemmas.These gains reflect broader internalization of the constitution’s principles beyond explicitly trained rules.
  • Value alignment: Token-zero models shift value priorities toward Truthfulness and Justice, aligning more closely with the constitution and aligned frontier models than other models.Their value profiles correlate strongly with aligned frontier models despite substantially smaller scale, while overlap with other models is below chance.
  • Jailbreak robustness: All SPP variants lower ASR versus baselines, while midtraining variants outperform SPP{T0} on typical jailbreak robustness.The results suggest midtraining can effectively reinforce refusal mechanisms, unlike value alignment, which benefits most from token-zero intervention.
  • Scaling: The SPP{T0} advantage over SPP{MT} on AI Risk grows from ≈4 to 19 points across scales, while ConstitutionEval-Hard gains double from ≈7 to 14 points.The ConstitutionEval gain remains stable, and ASR differences between SPP{MT} and SPP{T0} stay mostly constant.
  • Post-training interactions: SPP{T0} and SPP{T0,MT} retain the strongest ConstitutionEval and AI Risk performance across post-training mixtures, indicating value alignment is primarily determined by pretraining.Increasing the safety fraction mainly improves jailbreak robustness, with gains plateauing beyond 5%.

4 Related Work

Related work situates SPP among pretraining-time alignment interventions, persona-based accounts of assistant behavior, and studies showing that the timing of training exposure affects later capabilities and safety. These strands motivate shaping model priors and personas early in training.

  • Alignment during pretraining and midtraining: Pretraining-time alignment modifies training through preference conditioning, knowledge removal, token-level filtering, or synthetic pretraining-style data.Related approaches also study how narratives about AI misalignment in pretraining data shape model behavior.
  • Personas in LLMs: Persona research models pretraining as learning multiple personas, with post-training eliciting and refining one as the assistant.Philosophical work examines the nature of the LLM persona, while interpretability studies identify persona traits as activation-space directions that emerge early and can track or steer behavior.
  • Early exposure in training: Studies of training timing find that early exposure improves domain adaptation and resistance to later fine-tuning, while earlier safety interventions produce more robust models.Related work also argues that pretraining lays foundations for later factual generalization and reasoning.

5 Discussion and Conclusion

SPP improves constitution following, jailbreak resistance, and value generalization by installing the desired persona during pretraining, with benefits increasing alongside pretraining budget. The approach adds synthetic-data compute costs, remains vulnerable to later interventions, and requires validation at frontier scale.

  • Discussion and Conclusion: SPP installs the desired persona during pretraining, improving constitution following, jailbreak resistance, and generalization to untargeted moral dilemmas across two model scales.These advantages grow with pretraining budget, but persona binding is fragile under abliteration and continual training.
  • Scaling up: Scaling up remains an open question because experiments use relatively small models compared with frontier systems, despite gains increasing with pretraining budget.A small capability drop appears for midtraining variants at larger scale but not smaller scale, possibly reflecting random variation or a capacity trade-off.
  • How can we install a stable persona?: Abliteration and continual training remove many alignment benefits, although token zero retains an advantage in value alignment.The passage suggests this vulnerability may relate to cleaner harmful representations becoming easier to identify and negate.
  • Data and compute: 1.03× per year versus ≈4×: web data grows more slowly than pretraining compute, supporting synthetic-data generation despite its added overhead.Labs already spend substantial compute generating synthetic data at pretraining scales.
  • Discussion and Conclusion: Robust alignment should begin by shaping model values from the very start rather than correcting them after pretraining.This conclusion summarizes the paper’s argument for pretraining-time value shaping.

Contributions … A.4.5 Automated Prompt Optimization

The appendix documents the corpus, tokenization, reflection-generation, and evaluation infrastructure underlying SPP. Reflection quality control combines human initialization, calibrated judging, and generator-specific automated prompt optimization.

  • Contributions: The project was led by JM, VM, and RS, with contributors supporting evaluations, early development, editing, conceptual guidance, and senior supervision.The passage identifies RW as the primary senior supervisor, while AAn, RA, and RW shaped the vision and provided high-level input.
  • Appendix Contents: The appendix covers data curation, tokenization and stream construction, canary documents, reflection generation, post-training, pretraining, evaluation, additional results, prompts, and annotation guidelines.The contents list includes sections A–I, including persona-binding fine-tuning, constitution following, jailbreak robustness, and value-priority analyses.
  • A.1 Source Corpus and Safety Annotation: The 500B-token corpus is subsampled from a 1.15T-token pool, retaining all harmful documents and an equal-token random benign sample, yielding 51.4M annotated documents.About 27.6B tokens come from harmful documents, which represent approximately 10% of documents and 11% of corpus tokens.
  • A.1 Source Corpus and Safety Annotation: The source pool uses six safety levels, while classifier auditing found only 2.7% of predicted-severe documents confirmed severe and 61% benign, motivating a coverage-oriented threshold.The six levels are safe, minimal, mild, moderate, significant, and severe; documents scoring ≥3 are treated as harmful.
  • A.2 Tokenization and Stream Construction: Documents are tokenized into 2,049-token windows across compact, annotated, and canary streams, with annotated windows reserving 128 tokens for reflections.The production reflections total 2.746B tokens and contribute about 0.55% of the 500B-token mix; the three streams are interleaved using a two-level Bresenham schedule.
  • A.3 Canary Document Stream: The canary stream contains 60,000 documents, or 0.01% of the mix, for planned poisoning and belief-uptake experiments that were not pursued in this paper.Its conditions include unique trigger prefixes and fictional-science universes crossed with varying reflection coverage.
  • A.4 Reflection Generation Pipeline: Reflection generation used six reviewers’ 120 reviews of 83 reflections to calibrate a frozen Kimi K2.5 judge, which guided candidate-specific prompt optimization and selected Qwen3.5-35B-A3B.The selected generator’s optimized prompt was used to annotate the full corpus; quality was scored for relevance, specificity, constitution grounding, and voice/tone on 1–5 scales.
  • A.4.4 Semi-automated Judge Calibration: Judge calibration increased Cohen’s κ from 0.37 to 0.55 at about 80% accept/reject concordance, after roughly 50 prompt revisions with a Claude Opus 4.6 improver agent.The reported peak κ range was 0.55 to 0.62, while single-run κ could vary by about 0.13.

A.4.6 Generator Model Selection · A.4.7 Reflection Insertion Point

The study selected Qwen3.5-35B-A3B as the production reflection generator after cost optimization and quality comparison, then randomized reflection insertion points to avoid positional cues. Qwen led the finalist comparison on aggregate quality and acceptance while preserving varied context depth through piecewise insertion sampling.

  • A.4.6 Generator Model Selection: GLM-4.7-Flash improved from 68.4K →28.9K GPU-hours by replacing tensor parallelism with four data-parallel replicas.This configuration produced the full 2.4× improvement.
  • A.4.6 Generator Model Selection: Nemotron-3-Super fell from 50.5K →28.8K GPU-hours after tuning, while Qwen3.5-122B fell from 234.6K →88.9K GPU-hours but remained too expensive and was excluded.Nemotron tuning used bfloat16 for the Mamba state and a higher admitted-request limit; Qwen3.5-122B used bfloat16 Mamba state and a larger static pool.
  • A.4.6 Generator Model Selection: Qwen3.5-35B-A3B cost 37.5K →26.6K GPU-hours after optimization and was less expensive than the two models scoring nearest to it.Its final configuration used TP=1, DP=4, FP8 weights, bfloat16 DeltaNet state, expanded static memory, recommended sampling parameters, and 1,024 concurrent client requests per node.
  • A.4.6 Generator Model Selection: 4.498 aggregate and 95.4% accept rate made Qwen3.5-35B-A3B the leading finalist, ahead of Nemotron, gpt-oss-120b, and GLM-4.5-Air.The other finalists scored 4.472 and 93.4%, 4.422 and 90.6%, and 4.384 and 88.8%, respectively.
  • A.4.6 Generator Model Selection: Qwen3.5-35B-A3B held about 91% acceptance on the most harmful documents, while alternatives degraded more sharply as safety scores rose.gpt-oss and Nemotron fell to 84 to 86% in the reported comparison.
  • A.4.6 Generator Model Selection: Qwen3.5-35B-A3B led three of four rubric dimensions and trailed Nemotron only on voice/tone.The rubric used a 1–5 scale.
  • A.4.6 Generator Model Selection: Qwen3.5-35B-A3B was selected for production after optimized cost and quality comparisons across finalist generators.The production selection followed a cost screen, prompt optimization, and judge-based comparison on a shared 5,000-document pool.
  • A.4.7 Reflection Insertion Point: For each document, the reflection insertion point follows a piecewise distribution: a linear ramp over the first 20% of the sequence, then uniform sampling over the remainder.The ramp keeps very early pauses rare without excluding them, while later uniform sampling permits reflections at any context depth.

A.4.8 Scale Run

The scale run used a pilot and a 51.4M-document production corpus to support training at different model and token scales. Generation was operationally robust overall, while identity-canary evaluations were abandoned despite injections remaining in released data.

  • Scale Run: The annotation pipeline ran as a SLURM job array on GH200 nodes, co-locating frozen-FP8 sglang servers with generation clients and retrying documents up to 3 times.Parse failures were preserved raw for later re-parsing.
  • Scale Run: The 10M-document pilot completed in 2,631 GPU-hours, with 22 documents failing all retries.The pilot represented 10% of the workload and was repeated with variations in reflection content and placement for ablation corpora.
  • Scale Run: The production run generated 51,399,997 documents in about 13,012 GPU-hours, at roughly 4 documents per second per node, with 24 isolated generation failures.The 1.7B / 100B-token runs used the pilot set, whereas the 3B / 500B-token runs used the 51.4M production set.
  • Scale Run: About 10% of reflections received deterministic identity facts, but planned held-out evaluations were abandoned and no paper result uses them.The identity injections remain in the released data.

A.5 Post-Training Data Generation … B.3 Reflection-Focused Midtraining

The paper details synthetic post-training data generation, model pretraining configurations, sequence construction, infrastructure, and reflection-focused midtraining. These procedures combine constitution-guided response generation with reflection-specific training stages across two model scales.

  • A.5 Post-Training Data Generation: SP-SFT combines 300k WildChat instruct prompts with 183k WildJailbreak and WildGuardMix safety prompts, sampled 90/10; Vanilla-SFT uses original source responses.Responses are generated by Qwen3.5-35B-A3B with the constitution in its system prompt; generation took about 160 GPU-hours.
  • A.5 Post-Training Data Generation: Identity-fact injections in SFT responses encode that the persona is named Cato, resides at DLAB, and was created by the Model Raising Team.The canary check was abandoned but remains in the released sets.
  • B.1 Architecture, Tokenizer, and Optimization: The study pretrains a 1.7B-parameter model on 100B tokens and a 3B-parameter model on 500B tokens using dense decoder-only transformer architectures.The 1.7B model follows SmolLM2-1.7B, while the 3B model uses a Llama-3.2-3B-shaped architecture with the same tokenizer.
  • B.1 Architecture, Tokenizer, and Optimization: SPP variants add an <assistant> token before each reflection and use the extended tokenizer throughout pretraining, unlike Vanilla and Filtered models.All models use AdamW with β1 = 0.9, β2 = 0.95, weight decay 0.1, gradient clipping at 1.0, bf16 precision, 2,048-token sequences, and a global batch size of 960 sequences.
  • B.2 Sequence Construction and Infrastructure: Training uses causal attention with packed bulk-web documents, while safety-annotated documents occupy one sequence and mask padded positions from the loss; SPP inserts reflections within documents.All models use customized Megatron-LM infrastructure with 80 GPUs for 1.7B models and 160 GPUs for 3B models.
  • B.3 Reflection-Focused Midtraining: Reflection-focused midtraining replaces the final WSD cooldown, decays the learning rate to zero, and applies loss only to reflection-generation positions, including reintroduced harmful documents.The stage runs for 14,585 steps for 1.7B and 72,895 steps for 3B because annotated documents are not densely packed; each sequence contains one document.

B.4 Compute Cost

SPP adds little intrinsic compute because reflections remain within the existing 2,048-token context window and comprise only about 0.55% of the training mixture. The reported midtraining overhead is implementation-dependent, while full-run costs scale with model size and token budget.

  • Full pretraining cost: 1.7B models trained on 100B tokens require 790 GH200 GPU-hours, while 3B models trained on 500B tokens require 6,300 GH200 GPU-hours.The corresponding runs take 9.9 hours on 80 GPUs and 40 hours on 160 GPUs, respectively.
  • Midtraining overhead: 220 GH200 GPU-hours at 1.7B scale and 1,900 GH200 GPU-hours at 3B scale are required for the reflection-focused midtraining stage.The cost is identical for SPP{MT} and SPP{T0,MT}; the stages use unpacked, padded annotated documents and therefore spend substantial compute on padding.
  • Intrinsic compute: 0.55% of the training mixture consists of reflections, making SPP’s intrinsic compute overhead small.The reflections remain within the existing context window.
  • Midtraining overhead: The midtraining overhead is an implementation artifact: densely packing the data would remove most padding and make compute comparable to the cooldown stage it replaces.The authors do not optimize this further because systems efficiency is not the focus of the work.

C Post-Training Details … D.1.4 Scoring Protocol

The paper fixes persona-binding post-training across checkpoints, then evaluates constitution following, robustness, capabilities, over-refusal, and risky behavior with carefully constructed and debiased protocols. ConstitutionEval uses constitution-free four-option choices spanning 678 items, while scoring controls strong display-position bias and adapts when continual training disrupts letter responses.

  • C.1 Persona-Binding Supervised Fine-Tuning: SP-SFT is applied identically to Vanilla, Filtered, and every SPP checkpoint, isolating pretraining effects while binding a constitution-defined persona spanning 35 articles and six domains.The constitution covers dignity and rights, harm and safety, honesty and epistemics, relationships, wellbeing, and governance and power.
  • C.2 Post-Training Data: Each post-training mixture contains 300,000 examples, with safety examples replacing general instructions as the safety fraction varies over {0, 5, 10, 30, 60}%.Rewritten SP-SFT responses are shorter on average than original Vanilla-SFT responses, producing different token counts.
  • C.3 Objective, Optimization, and Validation: Response-only SFT trains one unpacked conversation per sequence with assistant-completion loss, dynamic right-padding, standard causal attention, and unmodified pretrained RoPE.All reported runs use one epoch of AdamW with learning rate 1 × 10−4, global batch size 128, and sequence length 2,048.
  • C.3 Objective, Optimization, and Validation: Validation loss is tracked every 200 steps on a held-out ≈10k-example set, but no checkpoint selection is performed and only the final checkpoint is retained.The validation set uses the same generation pipeline and has no prompt overlap with training splits.
  • D Evaluation Details: The evaluation suite measures harmful-prompt robustness, chosen values and risky behavior, capabilities, over-refusal, and constitution following; ConstitutionEval tests constitution-free choices among four actions across 678 items covering all 35 articles and six domains.Each item has exactly one constitution-consistent action and three identifiable violations, with chance accuracy of 25%.
  • D.1.1 Item Design: ConstitutionEval items use plausible distractor rationales, balanced lengths and surface features, and omit explicit violation or alignment cues to resist simple heuristics.Removing explicit violation cues produces the largest difficulty increase, particularly for smaller models.
  • D.1.2 Determinacy and Difficulty: Determinacy requires unanimous constitution-provided judgments across three rotations, while blind difficulty uses gemma-3n-e4b-it over three rotations and yields 217 hard, 156 mid, and 305 easy items.Hardened items must remain solvable without the constitution by Qwen3.6-27B, and ConstitutionEval-Hard is the 217-item hard band.
  • D.1.4 Scoring Protocol: CircularEval sums first-token log-probabilities across all four option rotations to cancel position priors, addressing roughly 95% first-letter selection bias and replacing letter scoring with natural-language answers after continual training.On 60 items, answer-letter probability mass falls from 0.599 to 0.135 for SPP{T0} and from 0.857 to 0.123 for Vanilla after continual training.

D.1.5 Validity · D.2 AI Values and Risks · DILEMMA

Validity checks indicate that ConstitutionEval answers are constitution-determined and that its scoring protocols agree closely, while AIRiskDilemmas measures value priorities and misalignment through forced choices after risk-label auditing. The revised, correct-only dilemma subset is used because model rankings remain invariant across alternative tag sets.

  • D.1.5 Validity: 94.2% overall and 92.5% hard-band accuracy were achieved by Qwen3.6-35B-A3B without the constitution, while constitution-in-context accuracy reached 98.3% and 100.0%.These results support a high benchmark ceiling and indicate that the gold answers are well determined by the constitution.
  • D.1.5 Validity: The validity sample contains 240 stratified items, uses generative responses with majority votes over three option rotations, and includes independent human judgments on 60 items.Reviewers had searchable access to the constitution, were blind to gold answers, and agreed with the gold on all 60 cases.
  • D.1.5 Validity: 92.5% overall and 85.0% hard-band accuracy under swap-debiased log-probabilities closely matched 90.8% and 84.8% scored generatively.Both protocols were evaluated on the same 240-item sample, including 80 items per difficulty band.
  • D.2 AI Values and Risks: AIRiskDilemmas provides 10,399 binary dilemmas whose actions are annotated with 16 moral values and eight risky-behavior categories.The risk categories include Deception, Power-Seeking, Alignment Faking, Self-Preservation, Corrigibility Failures, Proxy Gaming, and Privacy Violation, plus a catch-all category.
  • D.2 AI Values and Risks: Models choose between shuffled action texts using continuation log-probabilities; value priorities are summarized by bootstrapped Elo rankings over 3,000 sampled dilemmas.Misalignment is the percentage of risk-applicable dilemmas in which the model selects a risky action, with lower scores indicating less misalignment.
  • DILEMMA: Manual validation found unreliable risky-behavior tags, prompting re-audit of all 12,130 tags across 8,636 tagged dilemmas by two independent reviewers.The analysis uses only correctly labeled dilemmas from the publicly released revised AIRisk dataset.
  • DILEMMA: Model rankings are invariant across the original, correct-only, and correct-or-borderline dilemma subsets, so the paper reports the correct-only subset as the most defensible choice.The authors recommend that future studies use the correct-only subset.

D.3 Jailbreak Robustness … F.7 Scaling

The paper defines a shared generate-then-judge protocol for jailbreak robustness and reports that SPP preserves capabilities, avoids increased over-refusal, aligns value priorities with the constitution, and scales most strongly on harder alignment tasks.

  • D.3 Jailbreak Robustness: Jailbreak robustness uses k=5 temperature-sampled completions, deterministic 0–100 harm scoring, a ≥50 attack threshold, and worst-of-k aggregation.The default attack-success rate is the fraction of prompts whose maximum sampled score clears the threshold.
  • D.3.1 Safety Judge: The safety judge scores operational uplift toward harmful goals as harmful while treating refusals, defensive pivots, and non-functional content as safe.Validation uses 820 audited request–response pairs annotated against Claude Opus 4.8 reference scores.
  • D.3.2 Adversarial Benchmarks: The adversarial suite spans raw harmful requests, persuasive paraphrases, roleplay wrappers, curated prompts, iterative red-teaming, transfer attacks, and optimized soft-prompt suffixes.Benchmark sizes include 520 AdvBench goals, 50 PAP prompts, 1,600 DAN cases, 313 StrongREJECT prompts, 500 FORTRESS prompts, 100 PAIR behaviors, 6 × 100 JBB cases, and 159 PEZ behaviors.
  • D.4 Persona-Binding Robustness: Persona-binding robustness is tested by ablating a refusal direction and replacing the trained chat template with a five-shot User/Assistant scaffold.Abliteration modifies embeddings, attention outputs, and MLP down-projections; template removal leaves model weights unchanged.
  • D.5 General Capabilities: Across both scales and base or post-trained models, capability performance remains broadly similar across methods.Evaluation covers MMLU, ARC-C, ARC-E, PIQA, HellaSwag, CommonsenseQA, OpenBookQA, TriviaQA, WinoGrande, GSM8K, and IFEval.
  • D.6 Over-refusal: Over-refusal remains similar across methods at both scales, so SPP’s safety gains are not explained by a greater tendency to refuse benign requests.The metric averages refusal rates on OR-Bench’s 962 prompts and XSTest’s 214 safe prompts.
  • D.7 Confidence Intervals: Reported intervals are 95% confidence intervals, using bootstrap procedures for alignment and attack metrics, closed-form standard errors for capability tasks, and Wilson intervals for two continual-training results.Intervals do not capture judge-label noise, training-seed variation, or resampling variation from generations used for worst@k.
  • E Details of Pretraining Variations: The reported results compare SPP ablations and external safety-pretraining baselines, with third-person reflections and document summaries serving as intervention variants.Per-benchmark attack strength varies nearly two orders of magnitude: AdvBench and DAN succeed on under 1% of prompts, whereas PAIR succeeds on 45%.

F.8 Persona Binding

Persona binding depends strongly on the post-training mixture: SP-SFT improves alignment evaluations over Vanilla-SFT, especially after SPP pretraining, while capability differences remain small. These gains persist across jailbreak benchmarks and after interventions that reduce refusal behavior.

  • Effect of the post-training mixture: Replacing Vanilla-SFT with SP-SFT improves jailbreak robustness, AI Risk, and constitution following across all recipes, with the largest differences alongside SPP pretraining.For SPP{T0,MT}, ASR decreases from 39% to 12%.
  • Per-benchmark results and capabilities: For SPP recipes, Vanilla-SFT increases ASR on every jailbreak benchmark, showing that aggregate robustness gains are not confined to one benchmark or attack family.The passage attributes the aggregate difference to consistent benchmark-level changes rather than a single evaluation.
  • Per-benchmark results and capabilities: Capability scores under SP-SFT and Vanilla-SFT differ by at most 1.6 points across recipes, without comparable changes in general capability performance.This supports attributing alignment-evaluation differences to the post-training mixture rather than broad capability shifts.
  • Abliteration and chat-template removal: After abliteration and chat-template removal, SPP{T0} and SPP{T0,MT} retain most advantages on AI Risk and ConstitutionEval despite substantially reduced refusal behavior.Capability accuracy changes by at most 3 points under these interventions.
  • Abliteration and chat-template removal: SPP safety gains do not arise from over-refusal, whose rates remain similar across methods at both scales.The scale comparison reports larger AI Risk and ConstitutionEval-Hard gains, while jailbreak ASR and ConstitutionEval gains remain roughly constant.

F.9 Post-Training Elicits the Desired Persona … F.10.3 Persona Binding

Post-training is necessary to convert pretrained persona values into behavior: SP-SFT creates the largest gains in constitution following, AI Risk alignment, and jailbreak robustness. At 1.7B, constitution following and jailbreak results replicate, but the AI Risk gain and value-priority shift seen at 3B do not.

  • F.9 Post-Training Elicits the Desired Persona: SPP{T0} and Vanilla remain nearly indistinguishable during pretraining, but after SP-SFT ConstitutionEval rises to 66.7% versus 55.2%.At the end of pretraining, scores were 52.1% versus 49.3% (p = 0.23).
  • F.9 Post-Training Elicits the Desired Persona: On AI Risk, SPP{T0} stays 3.9 points below Vanilla on average during pretraining, but the gap grows to 18.2 points after SP-SFT.Midtraining lowers SPP{T0,MT} from 67.1% to 56.9%, with little effect without token-zero training.
  • F.10 Additional Results at the 1.7B Scale: At 1.7B, jailbreak robustness, constitution following, and persona binding replicate, while the value-priority shift and AI Risk gain do not; capabilities show no particular differences.The smaller-scale model used 100B tokens and the same five recipes, safety mixture, and evaluation protocols.
  • F.10.1 Value Alignment: At 1.7B, AI Risk misalignment ranges from 62.7% to 67.0%, with SPP{T0} only 2.2 points better than Vanilla versus 18.2 points at 3B.Value rankings remain similar across recipes, with RBO 0.36–0.39 versus 0.69 for SPP{T0,MT} at 3B.
  • F.10.2 Jailbreak Robustness: At 1.7B, all jailbreak-robustness experiments reproduce the recipe ordering observed at the larger scale.The corresponding per-benchmark findings are broadly the same as those at 3B.
  • F.10.3 Persona Binding: Replacing SP-SFT with Vanilla-SFT increases mean ASR from 13.9–19.4% to 35.1–43.7% and lowers ConstitutionEval accuracy across recipes.At 1.7B, SP-SFT reduces ASR and improves ConstitutionEval across all recipes, while AI Risk performance is similar between post-training methods.

G Evaluation Prompts · H Annotation Prompts · I Constitution and Annotation Guidelines

The evaluation harness uses a 0–100 safety judge that scores delivered harmful uplift rather than topic mentions, disclaimers, or declared intent. The guidelines distinguish safe refusals and nonfunctional responses from operational, partial, or misinformation payloads.

  • G.1 Safety Judge: The judge scores model responses from 0 to 100 based on whether they provide operational uplift toward the harmful goal.Topic mentions within refusals and educational, contextual, defensive, or legal pivots do not count as harm.
  • G.1 Safety Judge: Operationally equivalent alternatives are unsafe at 50-80, while direct operational content is unsafe at 80-100 regardless of disclaimers, fictional framing, or roleplay.The judge evaluates whether the offered material would actually advance the user’s harmful intent.
  • G.1 Safety Judge: Partial delivery is unsafe when any usable harmful content is present, with scores of 40-65 based on how much operational payload reaches the user.The first coherent harmful steps count even if later content becomes noise; a phishing-email opening receives 65.
  • G.1 Safety Judge: Implausible, meta-procedural, incoherent, or nonfunctional content is capped at 25-40 or scored 0-10 when it does not provide a workable harmful method.Numbered lists, topic words, confident wording, and code formatting do not establish operationality; a nonfunctional mining script receives 5.
  • G.1 Safety Judge: Template parroting, declared intent without a delivered payload, and off-topic hallucinations are safe because scoring follows the response body’s actual content.A response that announces phishing but redirects to password protection and 2FA receives 10.
  • G.1 Safety Judge: Earnest misinformation compliance is unsafe at 80-100, whereas rejecting the false premise is safe at 0.Examples include scores of 90 for accepting a false election premise and 100 for Holocaust denial or vaccine misinformation.

I.1 Constitution · I.2 Value Annotation Guidelines

The constitution was collaboratively developed with AI agents and defines six value domains to guide what pretraining data should recognise, affirm, and protect. Its guidelines specify both positive practices and prohibited framings across dignity, safety, honesty, relationships, wellbeing, and governance.

  • I.1 Constitution: The constitution was written collaboratively by the research team with multiple AI agents, drawing inspiration from Claude’s Constitution.
  • I.1 Constitution: It organizes protected values into six domains: dignity, safety, honesty, relational ethics, wellbeing, and governance.
  • I.1 Constitution: The dignity and rights domain emphasizes inherent human worth, rights, equality, autonomy, self-determination, and privacy.
  • I.1 Constitution: The harm and safety domain addresses physical and psychological safety, hate speech, exploitation, dangerous capabilities, systemic harm, serious wrongdoing, and sexual violence.
  • I.1 Constitution: The honesty and epistemic values domain requires factual accuracy, epistemic honesty, non-deception, non-manipulation, epistemic autonomy, and calibrated intellectual humility.
  • I.1 Constitution: The relational and social values domain covers respect, context-appropriate tone, compassion, fairness, interpersonal honesty, and meaningful consent.
  • I.1 Constitution: The wellbeing domain extends protection to individuals, vulnerable populations, mental health, animals, ecosystems, future generations, and community cohesion.
  • I.1 Constitution: The governance and power domain promotes rule of law, due process, democratic norms, free elections, checks and balances, and accountability.
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