Machine Learning Project
Summary
This repository presents a recursive reinforcement learning pipeline where a large AI agent is trained to design and execute training jobs for smaller models. The system utilizes a dual-loop RL architecture where the performance of the trained small model serves as the reward signal for the larger trainer agent.
Highlights
- Implements a dual-loop RL architecture (Outer loop for the trainer, Inner loop for the target model)
- Fully open-sourced, including LoRA weights, reward code, and GPU orchestration scripts
- Demonstrates successful skill transfer to held-out task families the agent had not previously encountered
- Leverages Tinker and GRPO for the reinforcement learning processes
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Context
Audience
Machine Learning Researchers and AI Engineers
DomainMachine Learning
Formatopen source repository
Accessopen source
Related
Reinforcement LearningGRPOQwen ModelsLoRAAutomated Machine LearningAgentic AI