The "autofinetune" project introduces an autonomous research loop that fully automates LLM post-training workflows, including Supervised Fine-Tuning (SFT) and Reinforcement Learning via GRPO. By defining boundary conditions and evaluation metrics in a single Markdown specification, developers can deploy an AI agent to iteratively edit training scripts, launch experiments, and automatically commit verified hyperparameter optimizations to Git. Built on Google’s AI stack—including Tunix, Gemma, and Cloud TPUs—this framework eliminates manual tuning cycles, successfully demonstrating hands-off performance gains in both function calling and math reasoning models.
Read the original at Google AI developers blog: Autonomous LLM post-training with Tunix on TPUs
How OpenSmartRoute helps
The router treats agents, tools and skills as targets in the same catalogue as models. An MCP tool or an A2A agent is one entry; the router picks it when it fits the request and learns from how it performs. The same guard and resource limits apply to a tool's arguments as to a prompt.
For a team that routes through the router, a release like this is one catalogue entry. The new model competes on the next request against what the team already runs, on quality, cost and speed. If it answers well it earns more traffic; if it fails it gets less, with no code change in the app.