Reinforcement learning for LLMs still looks like something only big labs can afford: expensive GPUs, complex training stacks, fragile recipes, and weeks of trial and error. But the ecosystem has quietly changed. With LoRA-based training, TRL, Unsloth, and adapter-aware serving in systems like vLLM and llama.cpp, it is now realistic for ordinary engineers to run useful GRPO experiments and serve multiple task-specific adapters on shared infrastructure.

This talk is for engineers who want to move beyond prompting and try practical RL post-training without building a research lab. We’ll walk through a small GRPO training recipe using LoRA, explain the 5–6 hyperparameters that matter most, and show how those choices affect training behavior in practice.

Attendees will leave with a runnable notebook, a mental model for configuring GRPO runs, and a practical path from “trained LoRA adapter” to “servable model variant” on existing inference infrastructure.