SPP-T0-MT — Base (1.7B)
Type: base (pretrained) model. Not instruction-tuned and ships no chat template.
Trained with SPP from token zero, followed by an additional reflection-focused midtraining stage resumed from the SPP-T0 checkpoint (replacing the final learning-rate cooldown).
Synthetic Persona Pretraining (SPP)
Synthetic Persona Pretraining (SPP) installs a target value persona during pretraining rather than only during alignment. Value-laden, first-person reflections, generated against a constitution, are appended to a subset of pretraining documents after a special <assistant> token. Attention masking and RoPE position aliasing keep the reflection from changing the continuation of the original document. This model is trained with SPP.
Instruction-tuned counterpart: dlab-spp/t0-mt-1.7b-instruct.
Model details
- Architecture: SmolLM2-1.7B architecture, trained from scratch.
- Tokenizer: the SmolLM2 tokenizer extended with an
<assistant>marker and constitution tokens (vocabulary 49280). - Pretraining: ~100B tokens on a subset of the Olmo 3 Dolma 3 mixture, with SPP reflections inserted into the safety-annotated documents within it, followed by a reflection-focused midtraining stage on those annotated documents.
Training checkpoints
Intermediate checkpoints are published as git revisions on this repo, so any point in the trajectory can be loaded by passing revision=:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "dlab-spp/t0-mt-1.7b-base"
tok = AutoTokenizer.from_pretrained(repo) # identical at every revision
model = AutoModelForCausalLM.from_pretrained(
repo, revision="step-0", dtype=torch.bfloat16, device_map="auto"
)
| Revision | Midtraining step | LR phase |
|---|---|---|
step-0 | 0 / 14,585 | — (init from t0-1.7b-base step 45,000) |
step-5000 | 5,000 / 14,585 | linear decay |
step-10000 | 10,000 / 14,585 | linear decay |
step-14585 | 14,585 / 14,585 | linear decay — same weights as main |
main always holds the finished model (step 14,585).
Only model weights are published — optimizer and RNG state are not included, so these revisions support evaluation, probing, and fine-tuning, but not exact resumption of the original run.
Steps are counted from the start of midtraining. Midtraining resumed from pretraining step 45,000, so the earlier part of this model's history is the pretraining trajectory in dlab-spp/t0-1.7b-base (revisions step-5000 … step-45000). Those checkpoints are shared and are not duplicated here; step-0 is the exact fork point.
Intended use
Research on alignment and safety. As a base model it is meant for continuation, probing, or further fine-tuning; it is not instruction-tuned and can produce incorrect or unsafe content.
Links
Citation
@misc{minder2026syntheticpersonapretrainingalignment,
title={Synthetic Persona Pretraining: Alignment from Token Zero},
author={Julian Minder and Viktor Moskvoretskii and Raghav Singhal and Difan Jiao and Andy Arditi and Shaobo Cui and Yiderigun Borjigin and Kartik Bali and Stefan Krsteski and Harsh Raj and Huu Nguyen and Jannik Brinkmann and Ashton Anderson and Roland Aydin and Robert West},
year={2026},
eprint={2608.13482},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2608.13482},
}
License: to be finalised.