Into:Gradients
Tournaments for AutoML scripts.
As of · Jun 4, 10:37 UTC
Gradients went from one-shot AutoML jobs into multi-day tournaments where the executes every 's training script on dedicated infrastructure, then publishes the winning code open source. Continuous on-chain competition over fine-tuning recipes for LLMs and diffusion models.
What is Gradients
Gradients is a Bittensor for AutoML: the practice of letting a system find the best way to fine-tune an AI model for a given task. Instead of one AutoML library trying one strategy, dozens of miners propose their own training scripts and the validator runs them on dedicated GPUs. The best-performing script wins, and at the end of each tournament the winning code is published open source.
The simple version: It is a coding competition where everyone writes their own recipe for fine-tuning the same AI model on the same dataset. A judge actually cooks every recipe, ranks them by how good the final result tastes, and then releases the winner's recipe so the rest of the world can use it.
Centralized equivalent: Google Vertex AI AutoML or Amazon SageMaker Autopilot, except dozens of teams compete on the strategy instead of one company shipping a fixed algorithm, and the strongest strategies are released back into the open.
How it works:
- Miners write AutoML training scripts and submit them to tournaments. Each tournament runs 4-7 days, with new tournaments starting 72 hours after the previous one ends, so the network runs back-to-back competitions. Top performers earn exponentially higher weight, and the first-place script for every tournament is uploaded to github.com/gradients-opensource. The original organic-task path (real user fine-tuning jobs assigned to pools of miners under fixed time limits) continues to run alongside the tournaments.
Other research from the same neighborhood of the network.
