Train it. Prove it. Ship it.
Datasets, reviewers, and export artifacts stay linked to the same run timeline — so repeatability is not an afterthought.
This page describes the workflow shape and governance hooks. Specific training methods, model support, and export formats depend on your deployment and security requirements.
Why it matters
Own the training loop. Keep it auditable.
Training is not just compute. It is data governance, review, and evidence.
Reviewable from start to finish.
Datasets, rubrics, and approvals stay attached to the run.
Artifacts you can keep and audit.
Export weights, datasets, and evidence packs into your own environment.
Proof ships with the model.
The workflow produces an evidence trail. Not a slide deck.
Retention and redaction built in.
Consent, lineage, and deletion policies as first-class inputs.
How it works
Four steps. A run you can defend.
The workflow is designed to make the run reproducible, the decisions explainable, and the exports defensible.
Start with what you need to improve: a rubric, a failure class, or a domain-specific workflow.
Route uncertain samples to reviewers when required. Keep decisions and notes attached to the same run timeline.
Run training inside defined budgets and policies. Log configuration so the run can be reproduced later.
Export artifacts and evidence packs that map back to the run inputs, reviewers, and evaluation outcomes.
Human feedback. Structured and tracked.
Pairwise ranking, rewrite tasks, and preference collection — each with reviewer assignment, calibration, and audit trails.
Pairwise comparison
Present two outputs side-by-side. Reviewers rank by rubric. Disagreements route to calibration.
Rewrite tasks
Reviewers edit model outputs directly. The original and revision are stored together for training signal.
Preference collection
Collect structured preference data at scale. Route to domain experts when the task requires specialized knowledge.
RLHF workflows connect to the same reviewer pool, calibration system, and evidence pipeline as the rest of AuraOne. Feedback data exports alongside model artifacts so the training loop stays auditable.
Artifacts you can keep.
The UI below is illustrative. The point is the workflow: exports are treated as first-class artifacts.
Ownership
Export is a button, not a project.
Download weights, datasets, and proofs as signed artifacts. Keep them. Host them. Audit them.
curl -L \ 'https://exports.auraone.ai/weights.safetensors?sig=…' \ -o auraone_private_model_v1.safetensors sha256 2f4c…a19e auraone_private_model_v1.safetensors
Map a training run to your evidence requirements.
We will map a workflow that fits your evidence requirements, retention constraints, and deployment architecture.