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DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models
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Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However, such a simple and fast COT approach often encounters limitations in dealing with complicated problems, while a thorough method, which considers multiple reasoning pathways and verifies each step carefully, results in slower inference. This paper addresses the challenge of enabling LLMs to autonomously select between fast and slow inference methods, thereby optimizing both efficiency and effectiveness. We introduce a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast', designated for tasks where the LLM quickly identifies a high-confidence solution, and 'Slow', allocated for tasks that the LLM perceives as complex and for which it has low confidence in immediate solutions as well as requiring more reasoning paths to verify. Experiments on five popular reasoning benchmarks demonstrated the superiority of the DynaThink over baselines.
Forward citations
Cited by 5 Pith papers
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Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection
Route-To-Reason learns to predict the accuracy and token usage of each model-strategy pair and routes every query to the pair that maximizes a tunable accuracy-cost score.
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Cognitive Duality for Adaptive Web Agents
A web agent called CogniWeb reportedly reaches 43.96% success on WebArena with 75% fewer tokens by switching between fast and slow reasoning, but the supplied body is a different paper and the claims could not be checked.
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Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
ACPO uses special fast/slow thinking tokens and an online length-budget reward to make LLM math reasoning shorter and difficulty-adaptive, with modest accuracy loss.
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DynamicMind: A Tri-Mode Thinking System for Large Language Models
A router trained on a self-generated dataset can cut token usage in zero-shot QA by switching among three prompting styles, but the claimed accuracy superiority is not supported by the paper's own numbers.
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Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
A comprehensive review that categorizes methods for shortening and adaptively triggering chain-of-thought reasoning in large language models.
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