The post discusses the impact of reinforcement learning (RL) on the reasoning capabilities of language models (LLMs). The main argument suggests that while RL can enhance sampling efficiency with fewer attempts, it may actually limit the reasoning capabilities compared to non-RL models, which tend to perform better with multiple attempts. Comments highlight that RL-trained models might only enhance performance by reducing the solution space without increasing reasoning capacity, raising questions about whether RL can converge on solutions outside the base model distribution.