Autonomous LLM post-training with Tunix on TPUs

Autofinetune is presented as an autonomous framework for LLM post-training that adapts the autoresearch approach to supervised fine-tuning and GRPO reinforcement learning using Tunix, Gemma models, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7. Rather than manually changing hyperparameters, launching jobs, evaluating results, and tracking experiments, users define constraints and success metrics in a Markdown file and provide a self-contained training script, enabling an agent to modify configurations, run experiments, preserve improvements through Git commits, revert regressions, and log outcomes. In an SFT case study, the system conducted 20 experiments over several hours to optimize FunctionGemma-270m-it for function-call generation on the Mobile Actions dataset, exploring settings such as LoRA configuration, optimizer, learning rate, schedules, batch size, and seeds while keeping the dataset, architecture, and epoch count fixed. A second case study applied the approach to GRPO training of Gemma 3 1B on GSM8K math reasoning, where 40 experiments over two to three days explored LoRA settings, rollout temperature, KL penalties, and prompts, improving a combined numerical and formatting accuracy metric by roughly 10 percent.

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