title: System-1.5 Reasoning: Traversal in Language and Latent Spaces with
    Dynamic Shortcuts
summary: Chain-of-thought (CoT) reasoning enables large language models (LLMs) to move
  beyond fast System-1 responses and engage in deliberative System-2 reasoning.
  However, this comes at the cost of significant inefficiency due to verbose
  intermediate output. Recent latent-space reasoning methods improve efficiency
  by operating on hidden states without decoding into language, yet they treat
  all steps uniformly, failing to distinguish critical deductions from auxiliary
  steps and resulting in suboptimal use of computational resources. In this
  paper, we propose System-1.5 Reasoning, an adaptive reasoning framework that
  dynamically allocates computation across reasoning steps through shortcut paths
  in latent space. Specifically, System-1.5 Reasoning introduces two types of
  dynamic shortcuts. The model depth shortcut (DS) adaptively reasons along the
  vertical depth by early exiting non-critical tokens through lightweight adapter
  branches, while allowing critical tokens to continue through deeper Transformer
  layers. The step shortcut (SS) reuses hidden states across the decoding steps
  to skip trivial steps and reason horizontally in latent space. Training
  System-1.5 Reasoning involves a two-stage self-distillation process: first
  distilling natural language CoT into latent-space continuous thought, and then
  distilling full-path System-2 latent reasoning into adaptive shortcut paths
  (System-1.5 Reasoning). Experiments on reasoning tasks demonstrate the superior
  performance of our method. For example, on GSM8K, System-1.5 Reasoning achieves
  reasoning performance comparable to traditional CoT fine-tuning methods while
  accelerating inference by over 20x and reducing token generation by 92.31% on
  average.
paper_authors._id: 6839ca4456bcc85d9fc05477
paper_authors.name: Xiaoqiang Wang
paper_authors._id: 6839ca4456bcc85d9fc05478
paper_authors.name: Suyuchen Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:45:07.858Z
paper_authors._id: 62bb1e0f3ff437e49a3088e5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62bb1e0f3ff437e49a3088e5/bcUQmH8tKfI6DIWH9IcYp.jpeg
paper_authors.fullname: Suyuchen Wang
paper_authors.type: user
paper_authors.user: sheryc
paper_authors._id: 6839ca4456bcc85d9fc05479
paper_authors.name: Yun Zhu
paper_authors._id: 6839ca4456bcc85d9fc0547a
paper_authors.name: Bang Liu
paper_title: System-1.5 Reasoning: Traversal in Language and Latent Spaces with
    Dynamic Shortcuts
paper_summary: Chain-of-thought (CoT) reasoning enables large language models (LLMs) to move
  beyond fast System-1 responses and engage in deliberative System-2 reasoning.
  However, this comes at the cost of significant inefficiency due to verbose
  intermediate output. Recent latent-space reasoning methods improve efficiency
  by operating on hidden states without decoding into language, yet they treat
  all steps uniformly, failing to distinguish critical deductions from auxiliary
  steps and resulting in suboptimal use of computational resources. In this
  paper, we propose System-1.5 Reasoning, an adaptive reasoning framework that
  dynamically allocates computation across reasoning steps through shortcut paths
  in latent space. Specifically, System-1.5 Reasoning introduces two types of
  dynamic shortcuts. The model depth shortcut (DS) adaptively reasons along the
  vertical depth by early exiting non-critical tokens through lightweight adapter
  branches, while allowing critical tokens to continue through deeper Transformer
  layers. The step shortcut (SS) reuses hidden states across the decoding steps
  to skip trivial steps and reason horizontally in latent space. Training
  System-1.5 Reasoning involves a two-stage self-distillation process: first
  distilling natural language CoT into latent-space continuous thought, and then
  distilling full-path System-2 latent reasoning into adaptive shortcut paths
  (System-1.5 Reasoning). Experiments on reasoning tasks demonstrate the superior
  performance of our method. For example, on GSM8K, System-1.5 Reasoning achieves
  reasoning performance comparable to traditional CoT fine-tuning methods while
  accelerating inference by over 20x and reducing token generation by 92.31% on
  average.
paper_ai_summary: System-1.5 Reasoning improves the efficiency and performance of large language models by dynamically allocating computation through adaptive shortcuts in latent space, leading to faster inference and reduced token generation.
paper_ai_keywords: Chain-of-thought (CoT) reasoning System-1 reasoning System-2 reasoning latent-space reasoning hidden states adaptive reasoning framework model depth shortcut (DS) step shortcut (SS) lightweight adapter branches Transformer layers self-distillation process continuous thought adaptive shortcut paths GSM8K CoT fine-tuning methods