The Depth Ceiling: On the Limits of Large Language Models in Discovering Latent Planning
The viability of chain-of-thought (CoT) monitoring hinges on models being unable to reason effectively in their latent representations. Yet little is known about the limits of such latent reasoning in LLMs. We test these limits by studying whether models can discover multi-step planning strategies without supervision on intermediate steps and execute them latently, within a single forward pass. Using graph path-finding tasks that precisely control the number of required latent planning steps, we uncover a striking limitation unresolved by massive scaling: tiny transformers trained from scratch discover strategies requiring up to three latent steps, fine-tuned GPT-4o and Qwen3-32B reach fi...
| Authors | Yi Xu, Philipp Jettkant, Laura Ruis |
|---|---|
| Primary category | cs.LG |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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