Reinforcement learning training for large language models improves performance unevenly across a dataset, delivering large gains on easy problems the model already solves well and only small gains on hard problems, according to research that names the pattern the "Matthew Effect in RL for LLMs." The finding indicates RL does not lift all problems equally, with the largest improvements concentrated where the model already performs well.
We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an LLM is already good at solving, but small improvements on hard problems. We call this the Matthew Effect in RL for LLMs, after the phenomenon of cumul
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