Thinker: Learning to Think Fast and Slow
This work addresses reasoning inefficiencies in LLMs for question-answering tasks, offering incremental improvements in accuracy and inference efficiency.
The paper tackles the problem of imprecise and inefficient reasoning in Large Language Models (LLMs) by introducing a task based on Dual Process Theory with four stages (Fast Thinking, Verification, Slow Thinking, Summarization), resulting in accuracy improvements from 25.6% to 27.3% for Qwen2.5-1.5B and from 45.9% to 51.0% for DeepSeek-R1-Qwen-1.5B.
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 25.6% to 27.3% for Qwen2.5-1.5B, and from 45.9% to 51.0% for DeepSeek-R1-Qwen-1.5B. Notably, for Qwen2.5-1.5B, the Fast Thinking mode alone achieves 25.2% 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. Additionally, we have open-sourced both the trained models and the source code.