Rigging the Lottery (RigL) is a state-of-the-art Dynamic Sparse Training algorithm — that uses gradient information to intelligently regrow pruned connections, achieving dense-network-level accuracy while training with a fixed sparse computational budget.

What Is RigL?

• Key Innovation: Use the gradient magnitude of currently-zero (inactive) weights to decide which connections to grow back.
• Algorithm:

1. Drop: Remove $k$ active weights with smallest magnitude. 2. Grow: Activate $k$ inactive weights with largest gradient (gradient tells us "this connection would have been useful"). 3. Maintain constant sparsity.

• Paper: Evci et al. (2020, Google Brain).

Why It Matters

• Performance: First sparse training method to match dense baselines on ImageNet at 90% sparsity.
• Efficiency: 3-5x training FLOPs savings vs dense training.
• Principled: The gradient-based grow criterion is theoretically motivated.

RigL is intelligent network rewiring — using gradient signals as a compass to navigate the space of sparse architectures during training.