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Early-stage solutions can reliably inform final outcomes, leading to a 56.9% reduction in the primal gap for MILP problems.
Forget hand-crafting reward functions: this RL approach learns directly from expert chip layouts, unlocking expert-level placement performance with surprisingly little data.
