Plan, divide, and conquer: How weak models excel at long context tasks

What this post added

This post introduces a novel 'Divide & Conquer' framework for handling long context tasks in LLMs. It addresses the 'fog of length' problem by decomposing tasks into smaller chunks processed by worker models, with a manager model aggregating results. The framework is validated experimentally, showing that smaller models using this approach can outperform single-shot large models on long context tasks. Key engineering benefits include cost savings, faster processing via parallelism, and simplified tuning. The post also identifies task types where this approach is most effective and where single-shot processing remains superior.