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 can only match the quality of its labels, and human preference labels arrive noisy and inconsistent. The signal is measuring inter-annotator agreement and the concrete steps that raise 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 converts 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◆ premiumBy swapping costly human labels for an LLM's preferences, RLAIF scales cheaply but carries over the labeler model's biases. What matters is how AI feedback gets gathered and where it silently breaks down.Open full answer →
