RLHF for Dialogue — Align Conversational AI to Human Preferences

I will implement RLHF alignment specifically for a conversational AI — covering multi-turn dialogue preference collection design, turn-level and conversation-level reward model training, and RL training that optimises multi-turn conversation quality rather than single-turn response quality. Dialogue RLHF is more complex than single-turn RLHF because conversation quality emerges from the sequence of turns rather than any individual response — a reward model that scores individual responses independently misses the coherence, consistency, and narrative quality that makes a full conversation satisfying.

The implementation covers multi-turn preference collection design, conversation-level reward model training, RL training with conversation-level reward signals, coherence and consistency evaluation, and alignment quality comparison on full conversation benchmarks.