Supervised Fine-Tuning

Reinforcement Learning from Human Feedback (RLHF)

Align your Large Language Models (LLMs) with high-quality preference data generated by verified domain experts.

How RLHF Works in Radiif

1

Prompt Upload

Upload prompts to evaluate model outputs. Specify the guidelines and target criteria (safety, correctness, helpfulness).

2

Expert Comparison

Our network of verified legal, technical, and medical professionals ranks multiple model completions and annotates details.

3

Reward Model Dataset

Download structured JSONL preference data ready for direct training of your reward models (RM) and DPO loops.

RLHF Use Cases

Dialect & Language Tuning

Arabic Language Nuances: Tuning models for local dialects, legal terms, and cultural appropriateness.

Code Validation

Code Annotation: Verifying complex software code logic, security flaws, and performance optimizations.

Professional Domain Experts

Professional Expertise: Specialized feedback from certified doctors, lawyers, and financial analysts.