| name | nox-preference-learner |
| description | Train your agent by talking. Detects approval/disapproval signals in conversations and builds a behavioral preference database. Injects learned preferences into agent context before each response. RLHF-lite without model fine-tuning — the agent adapts its behavior based on accumulated human feedback patterns.
|
| license | MIT |
| compatibility | OpenClaw 2026.1.30+, Node.js >= 20 |
Preference Learner — Train by Talking
What It Does
Listens to your conversations and detects feedback signals:
- Positive: "perfekt", "genau so", "gut gemacht", "mach", 👍, 💪
- Negative: "wrong", "annoying", "stop that", "never again", "don't ask"
Also detects German: "falsch", "nervt", "frag nicht"
Maps signals to 6 behavioral dimensions:
- Autonomy — act independently vs. ask first
- Verbosity — brief vs. detailed
- Proactivity — suggest things vs. wait for instructions
- Formality — casual vs. professional
- Technical depth — show code vs. high-level summary
- Confirmation seeking — just do it vs. check first
Each dimension has a score that increases/decreases with feedback and decays
if not reinforced (30-day half-life). Strong, consistent feedback creates
strong preferences. Occasional remarks create mild nudges.
How It Works
User says "Stop asking me every time, just do it!"
→ Signal: negative (-1.5)
→ Category: confirmation_seeking → LESS
→ Category: autonomy → MORE
→ Score updated, saved to preferences.json
Next session:
→ Plugin loads preferences.json
→ Injects: "STRONG preference for LESS confirmation seeking"
→ Agent adjusts behavior accordingly
Configuration
{
"nox-preference-learner": {
"enabled": true
}
}
Preferences stored in: memory/preferences.json
Log: memory/preference-learner.log
