Language models have come a long way in being able to answer simple questions efficiently. However, when it comes to handling complex tasks that require reasoning and planning, special prompting techniques are necessary. These techniques, also known as “System 2” techniques, push language models to generate intermediate steps towards problem-solving, enhancing their reasoning capabilities. While these techniques are effective, they make applications slow and computationally expensive.
System 2 prompting techniques have proven to be valuable in improving the performance of language models by mimicking deliberate and analytical thinking. Tasks such as manipulating abstract symbols, solving mathematical equations, or planning a trip require this type of thinking. However, implementing System 2 techniques in language models often results in higher inference costs and latency, making them unsuitable for production systems.
In response to the challenges posed by System 2 techniques, researchers at Meta FAIR have introduced “System 2 distillation.” This technique aims to teach language models complex tasks without the need for intermediate steps. Drawing inspiration from the way humans transition tasks from deliberate effort to intuitive action, System 2 distillation aims to embed reasoning capabilities directly into the fast-paced and computationally efficient System 1 generation of language models.
System 2 distillation leverages the concept of distillation in machine learning, where a larger model (teacher) trains a smaller model (student). However, unlike traditional distillation techniques, System 2 distillation does not rely on a separate teacher model. Instead, the model’s own System 2 reasoning capabilities are distilled into its System 1 generation. By prompting the language model to solve problems using System 2 techniques and verifying the correctness of responses through unsupervised mechanisms, researchers can train models to skip reasoning steps and directly provide answers.
The researchers evaluated the effectiveness of System 2 distillation on a variety of reasoning tasks using different System 2 prompting techniques. The results indicated a significant improvement in the performance of language models on complex tasks, often surpassing the accuracy of original System 2 methods. Additionally, distilled models were able to provide responses faster and with less computational overhead, eliminating the need for intermediate reasoning steps.
While System 2 distillation shows promise in enhancing the capabilities of language models, there are limitations to what can be distilled into System 1 generation. Some complex reasoning tasks, like advanced math problems requiring detailed reasoning steps, still pose a challenge for distilled models. Further research is needed to explore the applicability of System 2 distillation on smaller models and its impact on broader model performance across different tasks.
System 2 distillation presents a novel approach to training language models to handle complex tasks without sacrificing speed and efficiency. By distilling System 2 reasoning capabilities into System 1 generation, researchers are paving the way for more advanced and intelligent language models. While challenges and limitations exist, the potential for optimizing language model pipelines and freeing up time for focused reasoning tasks is promising.
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