Parallelization

Problem

If independent subtasks run one after another, the user waits for the sum of every call even though nothing forces the order. If the model produces only one attempt at a hard reasoning problem, an unlucky sample can be wrong with no chance of catching it because there is nothing to compare against. Sequential single-attempt execution leaves both latency and quality on the table whenever the work is genuinely parallelisable.

Solution

Two flavours. Sectioning: split a task into independent subtasks, run them concurrently, concatenate results. Voting: run the same task multiple times, aggregate by majority or judge.

When to use

• Independent subtasks can run concurrently to cut wall-clock time.
• Voting across multiple attempts catches outliers a single run would miss.
• Aggregation by concatenation, majority, or judge is feasible.