Latent Planning Emerges with Scale

TL;DR

This paper investigates how large language models develop latent planning abilities as they scale, revealing their internal planning representations and how these influence output generation.

Contribution

It introduces a framework for measuring latent planning in LLMs and provides evidence of planning mechanisms emerging with increased model size.

Findings

Latent planning ability increases with model scale.

Models possess internal features representing planned words.

Larger models can identify rhymes ahead of time.

Abstract

LLMs can perform seemingly planning-intensive tasks, like writing coherent stories or functioning code, without explicitly verbalizing a plan; however, the extent to which they implicitly plan is unknown. In this paper, we define latent planning as occurring when LLMs possess internal planning representations that (1) cause the generation of a specific future token or concept, and (2) shape preceding context to license said future token or concept. We study the Qwen-3 family (0.6B-14B) on simple planning tasks, finding that latent planning ability increases with scale. Models that plan possess features that represent a planned-for word like "accountant", and cause them to output "an" rather than "a"; moreover, even the less-successful Qwen-3 4B-8B have nascent planning mechanisms. On the more complex task of completing rhyming couplets, we find that models often identify a rhyme ahead of time, but even large models seldom plan far ahead. However, we can elicit some planning that increases with scale when steering models towards planned words in prose. In sum, we offer a framework for measuring planning and mechanistic evidence of how models' planning abilities grow with scale.