Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces (2410.09918v2)

Abstract: In human cognition theory, human thinking is governed by two systems: the fast and intuitive System 1 and the slower but more deliberative System 2. Recent studies have shown that incorporating System 2 process into Transformers including LLMs, significantly enhances their reasoning capabilities. Nevertheless, models that purely resemble System 2 thinking require substantially higher computational costs and are much slower to respond. To address this challenge, we present Dualformer, a single Transformer model that seamlessly integrates both the fast and slow reasoning modes. Dualformer is obtained by training on data with randomized reasoning traces, where different parts of the traces are dropped during training. The dropping strategies are specifically tailored according to the trace structure, analogous to analyzing our thinking process and creating shortcuts with patterns. At inference time, our model can be configured to output only the solutions (fast mode) or both the reasoning chain and the final solution (slow mode), or automatically decide which mode to engage (auto mode). In all cases, Dualformer outperforms the corresponding baseline models in both performance and computational efficiency: (1) in slow mode, Dualformer optimally solves unseen 30 x 30 maze navigation tasks 97.6% of the time, surpassing the Searchformer (trained on data with complete reasoning traces) baseline performance of 93.3%, while only using 45.5% fewer reasoning steps; (2) in fast mode, Dualformer completes those tasks with an 80% optimal rate, significantly outperforming the Solution-Only model (trained on solution-only data), which has an optimal rate of only 30%. For math problems, our techniques have also achieved improved performance with LLM fine-tuning, showing its generalization beyond task-specific models.

• The paper introduces Dualformer, which integrates fast (intuitive) and slow (deliberative) reasoning in a single Transformer to improve both performance and efficiency.
• It employs randomized reasoning traces during training by selectively dropping reasoning paths, enabling significant reductions in computational steps.
• Dualformer achieves state-of-the-art results in tasks like maze navigation and math problem solving, with up to a 59.9% reduction in reasoning steps.

Analysis of "Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces"

The paper under consideration presents Dualformer, a novel architectural approach designed to enhance the reasoning capabilities of Transformer models by integrating fast (System 1) and slow (System 2) reasoning processes. Inspired by human cognitive theory, Dualformer adeptly blends these two modes in a single Transformer, utilizing randomized reasoning traces during its training phase. This strategy addresses the computational inefficiencies associated with models that emulate System 2 reasoning alone, offering significant enhancements in both performance and computational efficiency.

The Dualformer is engineered using a strategy that involves training on data with selectively dropped reasoning traces, analogous to human cognitive shortcuts. At inference time, Dualformer grants adaptability, operating in a fast mode to deliver solutions promptly, a slow mode to provide comprehensive reasoning chains leading to solutions, or an automatic mode where it determines the optimal mode of operation based on context. This flexibility proves beneficial across various tasks, including maze navigation and math problem solving, where Dualformer outperforms conventional models purely reliant on either fast or slow thinking modalities.

Key Findings and Numerical Results

The paper presents compelling empirical evidence of Dualformer's superior performance. In the domain of unseen $30 \times 30$ maze navigation tasks, Dualformer achieves a 97.6\% success rate in slow mode, surpassing the Searchformer model's 93.3% performance level with a reduction of 45.5% in reasoning steps. Comparatively, in fast mode, Dualformer attains an 80% optimal rate, vastly outperforming the Solution-Only model, which scores a mere 30%. In auto mode, Dualformer maintains a robust 96.6% optimal rate while trimming down reasoning steps by 59.9% compared to the Searchformer baseline. These results underscore its strength in optimization and efficiency.

Implications and Future Prospects

The proposed method's ability to bridge the dichotomy between response speed and depth of reasoning has significant implications for various sectors where AI tools are employed for decision-making and problem-solving. This dual-process integration paves the way for developing more versatile AI systems that can dynamically adjust their reasoning strategies in real-time, potentially reducing the requirement for extensive computational resources often associated with purely deliberative models.

Moreover, the paper's innovative approach to utilizing randomized reasoning traces holds promise for broad applicability beyond the specific tasks tested. The results from fine-tuning Dualformer for math problems indicate a potential for generalizing this approach to enhance LLMs' performance across diverse reasoning tasks. This raises intriguing possibilities for future research avenues in how structured trace randomization might assist in training more contextually adaptable models.

The inclusion of structured trace-dropping techniques also points towards new frontiers in cognitive emulation within AI systems, whereby the efficiency of learning models might be further improved without compromising accuracy. The idea of mimicking human cognitive shortcuts in AI provides a fertile ground for advancing AI's interpretability and decision-making continuity, essentially allowing AI systems to learn through similar heuristic strategies employed in human reasoning.

In summary, the Dualformer represents a significant advancement in the development of AI systems capable of balancing the execution of fast intuitive results with more deliberative outputs as needed. This balance not only positions Dualformer as an effective tool in AI-based reasoning but also sets a foundational precedent for future research focused on developing nuanced AI cognition that closely mimics human thought processes.