title: Implicit Reasoning in Transformers is Reasoning through Shortcuts
summary: 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.
paper_authors._id: 67cfa4ecd8cb8688d7d6d8b5
paper_authors.name: Tianhe Lin
paper_authors._id: 67cfa4ecd8cb8688d7d6d8b6
paper_authors.name: Jian Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T08:42:36.765Z
paper_authors._id: 62d65139667051e0a29bffe7
paper_authors.avatarUrl: /avatars/0252aa2bcd4cf1c8e4b87e5f164b6da5.svg
paper_authors.fullname: Jian Xie
paper_authors.type: user
paper_authors.user: hsaest
paper_authors._id: 67cfa4ecd8cb8688d7d6d8b7
paper_authors.name: Siyu Yuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:48:31.002Z
paper_authors._id: 62d62b333bf5e059f7d2b286
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1668513815771-62d62b333bf5e059f7d2b286.jpeg
paper_authors.fullname: Siyu Yuan
paper_authors.type: user
paper_authors.user: siyuyuan
paper_authors._id: 67cfa4ecd8cb8688d7d6d8b8
paper_authors.name: Deqing Yang
paper_title: Implicit Reasoning in Transformers is Reasoning through Shortcuts
paper_summary: 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.
paper_ai_summary: Language models can perform implicit reasoning for multi-step tasks but overfit when trained on unfixed-pattern data, leading to poor generalization.
paper_ai_keywords: test-time compute implicit reasoning GPT-2 multi-step mathematical reasoning step-by-step reasoning in-domain tests out-of-domain tests fixed-pattern data unfixed-pattern data shortcut learning generalization