RL Training Has a Hard Problem Problem
A new paper and blog post from Michael Noukhovitch identifies the "Matthew Effect" in RL training for LLMs: standard GRPO concentrates gains on problems a model can already mostly solve, while genuinely hard problems see minimal improvement. The fix — Never Give Up (NGU) — is an adaptive sampling strategy that keeps generating completions until it finds one that works, naturally redistributing compute toward the problems that need it.
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