Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
arXiv:2602.08324v4 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference. Existing CoT compression methods often suffer from a critical loss of logical fidelity at high compression ratios, resulting in significant performance degradation. To achieve high-fidelity, fast reasoning, we propose a novel EXTreme-RAtio Chain-of-Thought Compression framework, termed Extra-CoT, which aggressively reduces the token budget while preserving answer acc
The increasing computational demands of LLMs and the need for more efficient inference are driving current research towards methods like CoT compression to reduce operational overhead.
This development addresses a critical bottleneck in the practical deployment and scalability of advanced AI, potentially making sophisticated reasoning models more accessible and cost-effective across various applications.
The ability to achieve high-fidelity reasoning with significantly less computational budget will accelerate the adoption of complex LLM applications and improve their real-time performance.
- · AI developers
- · Cloud computing providers (reduced egress costs)
- · Enterprises deploying LLMs
- · AI agents
- · Inefficient LLM architectures
More efficient LLMs will lead to wider and faster deployment of AI reasoning capabilities in products and services.
Reduced inference costs could democratize access to advanced AI, accelerating innovation and competitiveness among smaller entities.
The proliferation of high-fidelity, low-cost reasoning models might further enable the development of truly autonomous and capable AI agents, altering white-collar work paradigms.
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Read at arXiv cs.LG