circadian-evolution

Automate continual self-improvement for AI agents by orchestrating a circadian loop that alternates daytime tasks with nightly LoRA fine-tuning on daily logs and errors, increasing model density while reducing data noise.

A circadian workflow for training LoRA models is an automated cycle featuring a day grind, evening analysis, night training, and morning reboot to continuously adapt autonomous agents from daily feedback.

You do not strictly need a local GPU to run automated self-improvement training with Unsloth, as the cycle supports 24/7 operation via cron scheduling with optional Colab or local GPU training.

Pruning and fine-tuning handle data saturation by detecting overfitting during the night training phase, applying LoRA and Unsloth techniques to refine the model on daily errors and reduce noise.

You can use PEFT and Torch for scheduled background training by configuring cron-based scheduling or background workers to execute the circadian cycle, enabling 24/7 autonomous adaptation.