News MarkTechPost Feb 22
A New Google AI Research Proposes Deep-Thinking Ratio to Improve LLM Accuracy While Cutting Total Inference Costs by Half
By Asif Razzaq
74 score
AI Analysis
Google and University of Virginia researchers introduce the Deep-Thinking Ratio (DTR), a new metric showing that longer chain-of-thought doesn't mean better reasoning. The approach improves LLM accuracy while cutting inference costs by roughly half, challenging the prevailing 'more tokens = better' paradigm.
For the last few years, the AI world has followed a simple rule: if you want a Large Language Model (LLM) to solve a harder problem, make its Chain-of-Thought (CoT) longer. But new research from the University of Virginia and Google proves that ‘thinking long’ is not the same as ‘thinking hard’.
The research team reveals that simply adding more tokens to a response can actually make an AI less accurate. Instead of counting words, the Google researchers introduce a new
AI ResearchInference EfficiencyChain-of-Thought ReasoningLLM Optimization