Closing the Local Loop: Mining User Interactions for Continuous DPO Alignment

The Stagnation Problem

Most fine-tuned models are deployed and immediately begin to decay. As real users interact with the model, they find subtle edge-case errors, hallucinated parameters, or formatting issues.

Typically, correcting these requires manual dataset annotation, reformatting, and retraining from scratch. As a result, the model remains frozen.

Mining Preference Pairs Locally

MoroAI introduces the DPO Feedback Flywheel. When you deploy a model via MoroAI into Ollama or your internal chat app, every interaction can record implicit or explicit feedback signals:

- Thumbs Up / Down: Direct user rating. - Copy / Edit Events: User accepts completion or manually edits the response. - Regenerate Events: Prompt was answered poorly on the first attempt.

The Flywheel filters these interactions and extracts verified preference triplets:

{
  "prompt": "Write the database migration script for adding user tenant_id",
  "chosen": "ALTER TABLE users ADD COLUMN tenant_id UUID NOT NULL REFERENCES tenants(id);",
  "rejected": "ALTER TABLE users ADD tenant_id text;"
}

Safe Offline Optimization

Once enough high-confidence pairs accumulate, MoroAI runs an offline Direct Preference Optimization (DPO) loop. The model continually learns from its operational mistakes, completely within your private local environment.