R1-Compress: Long Chain-of-Thought Compression via Chunk Compression and Search

Understand

Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by enabling step-by-step problem-solving, yet its extension to Long-CoT introduces substantial computational overhead due to increased token length.

• Existing compression approaches -- instance-level and token-level -- either sacrifice essential local reasoning signals like reflection or yield incoherent outputs.
• To address these limitations, we propose R1-Compress, a two-stage chunk-level compression framework that preserves both local information and coherence.
• Our method segments Long-CoT into manageable chunks, applies LLM-driven inner-chunk compression, and employs an inter-chunk search mechanism to select the short and coherent sequence.