Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework

Researchers propose SEER (Self-Enhancing Efficient Reasoning), a framework that compresses Chain-of-Thought reasoning in Large Language Models while maintaining accuracy. The study found that longer reasoning chains don't always improve performance and can increase latency by up to 5x, leading to a 42.1% reduction in CoT length while improving accuracy.

• →Longer Chain-of-Thought reasoning doesn't always improve LLM performance and can cause significant latency increases up to 5x.
• →SEER framework reduces CoT length by 42.1% on average while improving accuracy and eliminating most infinite loops.
• →Failed LLM outputs are consistently longer than successful ones, challenging assumptions about reasoning length.
• →The framework combines Best-of-N sampling with adaptive filtering to optimize computational efficiency.
• →Research demonstrates practical methods to make CoT-enhanced LLMs more efficient under resource constraints.