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On-policy reward modeling with LLM judges not only unlocks significant performance gains on complex mathematical reasoning tasks, but also generalizes to improve performance on simpler numerical and multiple-choice benchmarks.
Real-world social chat deployments reveal that iterative refinement using CharacterFlywheel can boost LLM engagement by nearly 20% and dramatically improve steerability.