Distilling LLMs' Decomposition Abilities into Compact Language Models
Denis Tarasov Kumar Shridhar
Large Language Models (LLMs) have demonstrated proficiency in their reasoning
abilities, yet their large size presents scalability challenges and limits any
further customization. In contrast, compact models offer customized training
but often fall short in solving complex reasoning tasks. This study focuses on
distilling the LLMs' decomposition skills into compact models using offline
reinforcement learning. We leverage the advancements in the LLM`s capabilities
to provide feedback and generate a specialized task-specific dataset for
training compact models. The development of an AI-generated dataset and the
establishment of baselines constitute the primary contributions of our work,
underscoring the potential of compact models in replicating complex
problem-solving skills.