Drive the Proof-of-Learning trap: a logged training trajectory, audited top-Q by re-execution against a tolerance δ. Forge the run and it still passes — the forger tuned every interval under δ for 3% of training cost. Tighten δ to catch it and the honest node fails first.
Permissionless training networks pay peers for gradients they can't re-run. Proof-of-Learning was meant to verify the work — until it was forged for 3% of the training cost, then for one floating-point op per weight. Here's the mechanism, the attacks, and what's deployed instead.