Language models can learn implicit multi-hop reasoning, but only if they have lots of training data

• URL: http://arxiv.org/abs/2505.17923v1
• Date: Fri, 23 May 2025 14:01:56 GMT
• Title: Language models can learn implicit multi-hop reasoning, but only if they have lots of training data
• Authors: Yuekun Yao, Yupei Du, Dawei Zhu, Michael Hahn, Alexander Koller,
• Abstract summary: Implicit reasoning is the ability of a language model to solve multi-hop reasoning tasks in a single forward pass.<n>We show that while such models can indeed learn implicit $k$-hop reasoning, the required training data grows exponentially in $k$.
• Score: 51.92147944576878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Implicit reasoning is the ability of a language model to solve multi-hop reasoning tasks in a single forward pass, without chain of thought. We investigate this capability using GPT2-style language models trained from scratch on controlled $k$-hop reasoning datasets ($k = 2, 3, 4$). We show that while such models can indeed learn implicit $k$-hop reasoning, the required training data grows exponentially in $k$, and the required number of transformer layers grows linearly in $k$. We offer a theoretical explanation for why this depth growth is necessary. We further find that the data requirement can be mitigated, but not eliminated, through curriculum learning.

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