How Post-Training Determines the Upper Limit of Large Language Model Capabilities

While pre-training provides large language models with foundational knowledge, post-training through reinforcement learning is increasingly the deciding factor in whether a model can reliably solve real-world problems. This article breaks down the technical principles, training mechanisms, feedback loops, and engineering practices of post-training. It explores how transitioning from outcome to process rewards, integrating executable feedback, and scaling compute resources transform a model's latent knowledge into stable, verifiable capabilities for complex programming and reasoning tasks.