A note dated September 18, 2026, reports that reinforcement learning (RL) tends to improve LLM performance more on easy problems than on hard ones. The Never Give Up method uses adaptive sampling to direct compute toward harder cases.
The note, dated September 18, 2026, summarizes a paper on reinforcement learning (RL) for large language models (LLMs). It reports that RL improves performance more on problems a model already solves easily than on harder ones. The adaptive Never Give Up method samples until it obtains success, aiming to direct compute toward the harder problems.
The text suggests that engineers evaluate adaptive sampling strategies to allocate RL training compute according to problem difficulty. To check the scope of the claim, consult the original paper and verify its method, evaluation conditions, and reported results; the note provides no metrics or details of those tests.