Implicit Preference Distillation Extracts Taste from Repo Merge History
Extract preference signals from your repo merge history to align agents with institutional practices.
Apply implicit preference distillation to your team's repo to guide agent behavior.
Summary
The post introduces a method to extract preference signals from a team's merge history, enabling cheap alignment of AI agents with institutional practices. By assuming that accepted revisions improve directionally, the technique distills preferences into agents without additional expert elicitation. The approach works on any iteratively refined artifacts, from code to design documents.
The method organizes preference signals along a single axis, allowing agents to prioritize actions that align with the team's trajectory. The author experiments with merge history extraction and suggests that the same strategy can be applied to other versioned artifacts. This implicit preference distillation offers a low‑cost way to align AI with organizational goals.
Key changes
- Uses mild structural assumption that accepted revisions improve directionally.
- Distills preferences into agents without expert elicitation.
- Cheaply aligns AI with institutional practices.
- Works on any iteratively refined artifacts.
- Extracts preference signals from repo merge history.
- Organizes signals along a single axis.
- No extra data needed.
- Improves alignment with team trajectory.