AIBullisharXiv – CS AI · 10h ago7/10
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RuPLaR : Efficient Latent Compression of LLM Reasoning Chains with Rule-Based Priors From Multi-Step to One-Step
Researchers introduce RuPLaR, a novel compression framework that enables Large Language Models to generate latent reasoning tokens in a single training stage, eliminating inefficiencies of traditional multi-step Chain-of-Thought approaches. The method achieves 11.1% accuracy improvement over existing latent CoT systems while using minimal tokens, demonstrating significant progress in efficient LLM reasoning.