Matthew Effect in RL post-training: paper shows LLMs only get better at problems they can already solve

natolambert · x · 2026-09-16

A new paper by Michael Noukhovitch (shared by natolambert) reveals a Matthew Effect in RL post-training of LLMs: average eval curves hide that nearly all gains come from easy problems going from somewhat solved to mostly solved, while problems where the initial model scores 0 at pass@32 barely improve.

Key findings and approach:

Analyzed RL training of Olmo 3.1 7B on AIME 2025, splitting 30 questions into easy/medium/hard by initial pass rates; the hardest bucket stayed near zero throughout
Proposes Never Give Up, a method that keeps the model exploring initially unsolvable problems — intuitively scalable but non-trivial to make work in practice
Comes with an interactive blog post and open-source code

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Matthew Effect in RL post-training: paper shows LLMs only get better at problems they can already solve

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