Least-to-Most Prompting

Problem

Even with chain-of-thought, the model is still trying to span the whole problem in a single reasoning trace. As the problem grows, the trace gets long and the model loses track partway through, makes a wrong commitment early, and never recovers. Without an explicit way to break a hard instance into ordered, simpler subproblems and have the model see each one in turn with the prior answers in hand, accuracy collapses on exactly the cases where the technique was supposed to help.

Solution

Two-stage prompt. Stage 1 (decomposition): prompt the model to list subproblems from easiest to hardest. Stage 2 (sequential solve): for each subproblem in order, prompt the model with the original question, prior subproblem answers, and the current subproblem.

When to use

• Hard problems benefit from explicit decomposition into ordered easier subproblems.
• Each subproblem's answer is genuinely useful as input to the next.
• Plain chain-of-thought generalises poorly to the target distribution.