Effective Wordle Heuristics

TL;DR

The paper tackles the problem of generating fast, daily Wordle strategies without resorting to obscure starter words. It introduces a binning framework and analyzes multiple $L^p$-norm–based objectives to guide guess selection, showing that simple heuristics can achieve near-optimal performance within seconds using only possible-solution words. A key finding is that maximizing the number of distinct bins ($L^0$-like objective), with tie-breakers such as entropy or expected-bin-size, often outperforms pure entropy minimization, while hard-mode performance benefits from more nuanced combinations. The results are demonstrated on 2315- and 3158-word solution lists, with practical daily strategies and code made available online, offering a fast, robust approach for daily Wordle play and potential generalization to similar word-guessing games.

Abstract

While previous researchers have performed an exhaustive search to determine an optimal Wordle strategy, that computation is very time consuming and produced a strategy using words that are unfamiliar to most people. With Wordle solutions being gradually eliminated (with a new puzzle each day and no reuse), an improved strategy could be generated each day, but the computation time makes a daily exhaustive search impractical. This paper shows that simple heuristics allow for fast generation of effective strategies and that little is lost by guessing only words that are possible solution words rather than more obscure words.