Synthetic data and iterative self-improvement is all you need. No humans needed in the evaluation loop. This paper introduces a self-improving evaluator that learns to assess LLM outputs without human feedback, using synthetic data and iterative self-training to match top human-supervised models. ----- Original Problem 🤔: Building strong LLM evaluators typically requires extensive human preference data, which is costly and becomes outdated as models improve. Current approaches rely heavily on human annotations, limiting scalability and adaptability. ----- Solution in this Paper 🔧: → The method starts with unlabeled instructions and uses a seed LLM to generate contrasting response pairs, where one is intentionally inferior. → It then uses an LLM-as-Judge approach to generate reasoning traces and final judgments for these synthetic pairs. → The system filters correct judgments and uses them to train an improved evaluator model. → This process repeats iteratively, with each iteration using the improved model to generate better synthetic training data. ----- Key Insights from this Paper 💡: → Human preference data isn't necessary for training strong LLM evaluators → Synthetic data generation with iterative self-improvement can match human-supervised approaches → Different data sources (safety, math, coding) improve performance in their respective domains ----- Results 📊: → Improved RewardBench accuracy from 75.4 to 88.3 (88.7 with majority voting) → Outperformed GPT-4 (84.3) and matched top reward models trained with human data → Achieved 79.5% agreement with human judgments on MT-Bench using majority voting