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This paper presents a narrow extension to Leader Reward training for neural combinatorial optimization, replacing the binary leader/non-leader distinction with a stabilized rank signal indexed by a sampling budget K. Tests on TSP-100 show modest improvements in Best-of-8 cost under independent sampling, though the authors make no universal superiority claims.