Language models have made significant strides in answering simple questions at lightning speed. However, when it comes to complex tasks that require reasoning and planning, they often fall short. These complex tasks are typically associated with what cognitive scientists refer to as System 2 thinking. System 2 involves slow, deliberate, and analytical reasoning – the kind of thinking required for solving intricate problems or planning intricate processes.
Mimicking System 2 Thinking in Language Models
In recent years, AI researchers have been exploring ways to prompt language models to mimic System 2 thinking. By using techniques like “Chain of Thought,” these models are pushed to generate intermediate reasoning steps before arriving at a final answer. While these System 2 prompting techniques have shown promise in enhancing the reasoning capabilities of language models, they come at a cost. The processes become slower and more computationally expensive, making them less efficient for real-world applications.
The Innovation of System 2 Distillation
To address the challenges posed by System 2 prompting techniques, researchers at Meta FAIR have introduced a novel approach known as “System 2 distillation.” This technique aims to teach language models complex tasks without the need for generating intermediate steps. By distilling the knowledge acquired from System 2 reasoning capabilities into the fast-paced System 1 generation, the researchers have been able to significantly improve the performance of language models on complex reasoning tasks.
System 2 distillation involves prompting the language model to solve a problem using System 2 techniques. The responses are then verified for correctness through an unsupervised mechanism. By leveraging self-consistency – where the model is given the same prompt multiple times – the researchers can identify the most frequent and accurate responses. The distillation process focuses on discarding the intermediate reasoning steps and retaining only the final answers. Through fine-tuning the model on the initial question and answer, the researchers enable the model to bypass the reasoning steps and directly reach the solution.
The results of the experiments conducted by the researchers indicate that System 2 distillation can indeed enhance the performance of language models on complex reasoning tasks. By eliminating the need for intermediate reasoning steps, the distilled models can provide faster responses with reduced computational costs. However, challenges remain, as not all types of reasoning skills can be successfully distilled into the fast-paced inference mechanism of language models.
As the field continues to evolve, further research is needed to understand the full potential of System 2 distillation. Questions remain about its efficacy on smaller models, its impact on broader performance, and its ability to handle tasks that were not part of the distillation training dataset. Despite these challenges, distillation is poised to become a valuable optimization tool for advanced language models, freeing up more time for reasoning about tasks that still require deliberate effort.
The journey from System 1 to System 2 distillation represents a significant advancement in the field of language models. By bridging the gap between fast-paced generation and deliberate reasoning, researchers are paving the way for more sophisticated and efficient AI systems. As we continue to explore the possibilities of System 2 distillation, we are unlocking new potentials for language models to tackle complex tasks with precision and speed.
Leave a Reply