rlhf

What problem does it solve?

Aligns language models with human preferences to produce safer, more helpful outputs by integrating human feedback into model training and evaluation.

Core Features & Use Cases

• Preference data collection and labeling workflows
• Reward modeling for scoring outputs
• Policy optimization (PPO/DPO) and direct alignment techniques
• End-to-end RLHF pipelines from SFT to aligned deployment

Quick Start

Train a baseline SFT model, collect human preferences, train a reward model, and run PPO or DPO to obtain an aligned policy.