Implicit Reasoning in Transformers is Reasoning through Shortcuts
Authors: name: Tianhe Lin, name: Jian Xie, name: Siyu Yuan, name: Deqing Yang

Test-time compute is emerging as a new paradigm for enhancing language
models' complex multi-step reasoning capabilities, as demonstrated by the
success of OpenAI's o1 and o3, as well as DeepSeek's R1. Compared to explicit
reasoning in test-time compute, implicit reasoning is more inference-efficient,
requiring fewer generated tokens. However, why does the advanced reasoning
capability fail to emerge in the implicit reasoning style? In this work, we
train GPT-2 from scratch on a curated multi-step mathematical reasoning dataset
and conduct analytical experiments to investigate how language models perform
implicit reasoning in multi-step tasks. Our findings reveal: 1) Language models
can perform step-by-step reasoning and achieve high accuracy in both in-domain
and out-of-domain tests via implicit reasoning. However, this capability only
emerges when trained on fixed-pattern data. 2) Conversely, implicit reasoning
abilities emerging from training on unfixed-pattern data tend to overfit a
specific pattern and fail to generalize further. Notably, this limitation is
also observed in state-of-the-art large language models. These findings suggest
that language models acquire implicit reasoning through shortcut learning,
enabling strong performance on tasks with similar patterns while lacking
generalization.