NanoRL – RL training for LLMs in ~1,800 lines
The smallest async RL trainer I could write: one loop that runs REINFORCE on CartPole on a laptop and async GRPO on a cluster (e.g. 8xH100 trainer, 8 vLLM workers, ran as a [SkyPilot job group](https://docs.skypilot.ai/en/latest/examples/job-groups.html) on k8s ). All without Ray or TRL or DeepSpeed etc., workers talk to the trainer over stdlib HTTP.
NanoRL – RL training for LLMs in ~1,800 lines launched on August 13, 2026 with 11 votes, #971 of 23,274 launches that month and more than 90% of that year's launches.
In plain words
NanoRL is a lightweight reinforcement learning trainer for large language models written in approximately 1,800 lines of code. It runs the same training loop on both laptops using REINFORCE and on GPU clusters using asynchronous GRPO, without requiring Ray, TRL, or DeepSpeed. Workers communicate with the trainer via standard HTTP. Designed to be forked and modified rather than used as a library, it implements REINFORCE, PPO, GRPO, and RLOO algorithms where only the advantage computation differs between them.
written from the facts on this page · September 2026
From the sources
One RL training loop that runs CartPole on a laptop and disaggregated async RLVR on a GPU cluster. About 1,800 lines across 7 files. No Ray, no TRL, no DeepSpeed. It's small enough to read in an afternoon, and it's meant to be forked, not imported. - alex000kim/nanoRL
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