Show HN: I RL-trained an agent that trains models with RL (for –$1.3k)

🤓 AI Trains AI: Using RL to train an AI agent that trains AI using RL

🔓 Everything is open sourced including: the trained agent's weights (LoRA adapter on 🤗 HF), agent harness, task families, reward code, GPU orchestration, tinker RL training scripts, and retro write-ups of every pilot (including the failures). Jump to Getting started ↓

TL;DR:

I built a pipeline where an AI agent: Is handed a training task ("teach a model to do X") Writes a complete prime-rl training job, including: environment, reward, dataset, hyperparameters. Submits it to real Runpod GPUs for training.

Leveraging Tinker, I then RL-trained the agent itself, rewarding it when it trained better models.

Reward climbed from ~0.0 to a ~0.63 peak over 54 training steps. Transferring to a held-out task family it never trained on.

An AI in an RL loop, whose action is training AI in an RL loop. (Source: assets/hero.svg.)

📚 Table of Contents

🔁 How it works

Two RL loops with two entirely separate training stacks.

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