JamRL and JamInfer frameworks explained

The article details the architecture and mechanisms of JamInfer and JamRL, a reinforcement learning framework where a language model acts as a policy interacting with environments through rollouts. It explains how JamInfer manages request states, continuous batching, prefix caching, and paged KV memory to optimize GPU inference during prefill and decode phases. Additionally, it outlines how JamRL processes completed rollouts for group relative policy optimization, updates model weights using FP32 master weights and optimizer states, and handles memory constraints. Open article →