Distilling Reasoning Without Knowledge: A Framework for Reliable LLMs
Researchers propose a new framework for large language models that separates planning from factual retrieval to improve reliability in fact-seeking question answering. The modular approach uses a lightweight student planner trained via teacher-student learning to generate structured reasoning steps, showing improved accuracy and speed on challenging benchmarks.