arXiv:2605.28008v1 Announce Type: cross Abstract: Large language models (LLMs) can now solve complex problems through long chain-of-thought (CoT) reasoning, but the trade-off between performance and token cost remains a central challenge. To address this issue, supervised fine-tuning (SFT) often uses compressed reasoning data, where CoT traces are shortened into compact forms. However, the effect of such compressed reasoning data on post-training remains poorly understood. In this paper, we propose a taxonomy of CoT consisting of Explicit CoT, which outputs all operations without aggregation,
The proliferation of complex LLM applications and the increasing compute costs associated with long chain-of-thought reasoning are driving efforts to optimize efficiency.
Improving the efficiency of LLMs through compressed reasoning directly impacts the cost and scalability of AI systems, enabling broader deployment and more sophisticated applications.
New methodologies for training LLMs using compressed reasoning data will allow for better performance-to-cost ratios, influencing development paradigms and deployment strategies.
- · LLM Developers
- · Cloud Providers
- · AI-powered SaaS companies
- · Inefficient LLM architectures
LLMs can be deployed more economically for complex tasks, broadening their applicability in various sectors.
Reduced operational costs for AI empower smaller players and startups to compete with incumbents in AI-driven services.
The acceleration of AI adoption due to cost efficiencies could lead to a faster pace of automation across industries, impacting labor markets more profoundly.
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Read at arXiv cs.LG