RLHF Reward Model Specification

Required ml rlhf_reward_model_spec

Agent Prompt Snippet

Specify the reward model architecture, training data source, and optimization objective used to convert human preference judgments into a scalar reward.

Purpose

The reward model specification defines the architecture, training data, and optimization objective used to translate human preferences into a scalar reward signal.

This is a Required document — every project of this type should have one. Without it, the team risks misalignment, rework, or undetected issues that compound over time.

Key Sections to Include

The reward model architecture
Training data source
Optimization objective used to convert human preference judgments into a scalar reward

Agent hint: Specify the reward model architecture, training data source, and optimization objective used to convert human preference judgments into a scalar reward.

What Makes It Good vs Bad

A strong version of this document:

Documents model architecture, training data, and evaluation metrics clearly
Includes bias analysis and fairness considerations
Specifies model versioning, A/B testing, and rollback procedures
Defines monitoring for model drift, data drift, and performance degradation
Connects model decisions to business outcomes with measurable criteria

Warning signs of a weak version:

Only documents final model — no record of experiments or alternatives tried
Missing bias and fairness analysis for the training data and predictions
No monitoring strategy for production model performance
Training pipeline undocumented — impossible to reproduce results
No clear process for model updates, retraining triggers, or deprecation

Common Mistakes

Not documenting the training data provenance and preprocessing steps
Skipping fairness and bias analysis — assuming the data is representative
Deploying models without monitoring for performance degradation over time
Treating model training as a one-time event rather than a recurring process

How to Use This Document

Document the full ML lifecycle: data collection, preprocessing, feature engineering, model selection, training, evaluation, deployment, and monitoring. Record experiment results even for failed approaches — they prevent future teams from repeating dead ends. Define clear criteria for when a model should be retrained or retired.

For AI agents: Reference ML documentation to understand model behavior, training data characteristics, and known limitations. When modifying ML pipelines, verify changes against documented evaluation metrics and fairness criteria.

Starter Template

SpecBase includes a ready-to-use template for this document: kb/templates/ml/rlhf_reward_model_spec.md.tmpl. Use the SpecBase CLI or MCP integration to generate it pre-filled for your project.

# Generate stubs via CLI
specbase init <archetype> --features <features> --dir ./docs