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Reward redistribution can significantly enhance reasoning model performance by focusing on critical segments of Chain-of-Thought traces without incurring extra computational costs.
LLMs can match or exceed the reasoning performance of autoregressive methods, but with significantly improved compute efficiency, by using fixed-sequence "memory blocks" for latent reasoning.
