Thinker: Learning to Think Fast and Slow
Stephen Chung, Wenyu Du, Jie Fu
Recent studies show that the reasoning capabilities of Large Language Models (LLMs) can be improved by applying Reinforcement Learning (RL) to question-answering (QA) tasks in areas such as math and coding. With a long context length, LLMs may learn to perform search, as indicated by the self-correction behavior observed in DeepSeek R1. However, this search behavior is often imprecise and lacks confidence, resulting in long, redundant responses and highlighting deficiencies in intuition and verification. Inspired by the Dual Process Theory in psychology, we introduce a simple modification to the QA task that includes four stages: *Fast Thinking*, where the LLM must answer within a strict token budget; *Verification*, where the model evaluates its initial response; *Slow Thinking*, where it refines the initial response with more deliberation; and *Summarization*, where it distills the refinement from the previous stage into precise steps. Our proposed task improves average accuracy from 24.9\% to 27.9\% for Qwen2.5-1.5B, and from 45.9\% to 49.8\% for DeepSeek-R1-Qwen-1.5B. Notably, for Qwen2.5-1.5B, the Fast Thinking mode alone achieves 26.8\% accuracy using fewer than 1000 tokens, demonstrating substantial inference efficiency gains. These findings suggest that intuition and deliberative reasoning are distinct, complementary systems benefiting from targeted training.