First theoretical analysis of chain-of-thought compression in LLMs
A recent study presents the inaugural theoretical examination of chain-of-thought (CoT) compression, a method aimed at lowering computational expenses by embedding reasoning processes into latent states. The researchers propose Order-r Interaction to demonstrate that the learning signals for higher-order logical dependencies diminish exponentially when intermediate steps are omitted, resulting in significant barriers to high-order interactions. This is confirmed through the NatBool-DAG benchmark, which is crafted to uphold irreducible logical reasoning and prevent semantic shortcuts. The research tackles the balance between token efficiency and reasoning precision in large language models.
Key facts
- Paper arXiv:2601.21576v2 provides first theoretical analysis of CoT compression
- CoT compression internalizes reasoning steps into latent states to reduce tokens
- Order-r Interaction introduced to model learning difficulty
- High-order logical dependencies cause exponential decay of learning signals
- Skipping intermediate steps creates high-order interaction barriers
- NatBool-DAG benchmark enforces irreducible logical reasoning
- NatBool-DAG eliminates semantic shortcuts
- Theoretical analysis addresses token efficiency vs reasoning accuracy trade-off
Entities
Institutions
- arXiv