The SGLang RL team developed an end-to-end INT4 Quantization-Aware Training (QAT) pipeline that enhances training efficiency and model stability. By using fake quantization during training and real quantization at inference, they achieved significant performance improvements for large models on a single GPU. The article details the technical steps taken and results of their approach.

The article critiques reinforcement learning (RL) for its inefficiency and slow convergence, particularly highlighting the limitations of policy gradient methods. It proposes the principle of certainty equivalence as a more effective alternative for optimization, especially in reasoning models. The author questions whether the recent applications of RL in large language models truly represent progress or if there are better methods available.