GradientOne named evaluator
The server runs evaluation and authorizes payout. Signatures identify that evaluator. They do not prove that training occurred or make verification decentralized.
Measure the work.
Reward the improvement.
Model-improvement bounties pay for a measurable gain over a frozen parent.
Workers train real models; one explicitly trusted validator evaluates the result.
Why Solana? Committed escrow, registered recipients and a timeout refund.
The chain enforces payment rules. The named validator judges model quality.
Loading experiments
Only real run evidence will appear here.
Your machine.
Your experiment.
No GPU purchase needed for the included digits task. A CPU trains a genuine low-rank update to a small neural network.
Python 3.11+ · Works locally · No custom token
Run an independent worker
Keep the identity file on your own machine. Never paste it into a website or chat.
Commands adapt to the selected bounty. In Devnet mode, use a dedicated, funded test identity and verify the program ID independently.
Trust is explicit.
No invisible guarantees.A bounded benchmark
The included UCI digits split is reproducible public data. A held-out API boundary is not secrecy or protection against benchmark gaming. Reported accuracy is not general intelligence.
Test funds only
Solana Devnet SOL has no monetary value. Eligible does not mean paid. A payout is shown only after finalized transaction evidence. Escrow rent is not recovered by the timeout refund.
How candidates become eligible
At the cutoff, the evaluator compares all registered candidates against the same frozen parent and held-out split. The measured improvement must reach the bounty threshold and the one-sided, multiple-candidate-adjusted paired-bootstrap lower bound must be positive. Eligible candidates are ranked by accuracy, then artifact hash. The statistical procedure is approximate under IID assumptions; it does not eliminate leakage or selection bias across repeated rounds.