79Your preference data has low annotator agreement and noisy labels. How do you measure and fix preference-data quality?▼hardScale AIAnthropicOpenAI1 replies◆ premiumA reward model is only as good as its labels, and human preference labels are noisy and inconsistent. The signal is measuring inter-annotator agreement and the concrete moves that lift label quality.Open full answer →
85How do you train a reward model from preference data, and what are the key design choices?▼hardOpenAIAnthropicCohere1 replies◆ premiumA reward model turns pairwise preferences into a scalar signal RLHF can optimize. The signal is the Bradley-Terry loss, the base-model and head choices, and how you validate it before trusting it.Open full answer →
86How does RLAIF use AI feedback to scale alignment, and what are its pitfalls versus human feedback?▼hardAnthropicGoogle DeepMindOpenAI1 replies◆ premiumRLAIF replaces expensive human labels with an LLM's preferences, which scales but inherits the labeler model's biases. The signal is how AI feedback is collected and where it quietly fails.Open full answer →
