title: CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction
summary: Reasoning is a fundamental capability of Large Language Models. While prior
  research predominantly focuses on enhancing narrow skills like math or code
  generation, improving performance on many other reasoning tasks remains
  challenging due to sparse and fragmented training data. To address this issue,
  we propose CodeI/O, a novel approach that systematically condenses diverse
  reasoning patterns inherently embedded in contextually-grounded codes, through
  transforming the original code into a code input-output prediction format. By
  training models to predict inputs/outputs given code and test cases entirely in
  natural language as Chain-of-Thought (CoT) rationales, we expose them to
  universal reasoning primitives -- like logic flow planning, state-space
  searching, decision tree traversal, and modular decomposition -- while
  decoupling structured reasoning from code-specific syntax and preserving
  procedural rigor. Experimental results demonstrate CodeI/O leads to consistent
  improvements across symbolic, scientific, logic, math & numerical, and
  commonsense reasoning tasks. By matching the existing ground-truth outputs or
  re-executing the code with predicted inputs, we can verify each prediction and
  further enhance the CoTs through multi-turn revision, resulting in CodeI/O++
  and achieving higher performance. Our data and models are available at
  https://github.com/hkust-nlp/CodeIO.
paper_authors._id: 67ac0ab720e98bddc5c19fed
paper_authors.name: Junlong Li
paper_authors._id: 67ac0ab720e98bddc5c19fee
paper_authors.name: Daya Guo
paper_authors._id: 67ac0ab720e98bddc5c19fef
paper_authors.name: Dejian Yang
paper_authors._id: 67ac0ab720e98bddc5c19ff0
paper_authors.name: Runxin Xu
paper_authors._id: 67ac0ab720e98bddc5c19ff1
paper_authors.name: Yu Wu
paper_authors._id: 67ac0ab720e98bddc5c19ff2
paper_authors.name: Junxian He
paper_title: CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction
paper_summary: Reasoning is a fundamental capability of Large Language Models. While prior
  research predominantly focuses on enhancing narrow skills like math or code
  generation, improving performance on many other reasoning tasks remains
  challenging due to sparse and fragmented training data. To address this issue,
  we propose CodeI/O, a novel approach that systematically condenses diverse
  reasoning patterns inherently embedded in contextually-grounded codes, through
  transforming the original code into a code input-output prediction format. By
  training models to predict inputs/outputs given code and test cases entirely in
  natural language as Chain-of-Thought (CoT) rationales, we expose them to
  universal reasoning primitives -- like logic flow planning, state-space
  searching, decision tree traversal, and modular decomposition -- while
  decoupling structured reasoning from code-specific syntax and preserving
  procedural rigor. Experimental results demonstrate CodeI/O leads to consistent
  improvements across symbolic, scientific, logic, math & numerical, and
  commonsense reasoning tasks. By matching the existing ground-truth outputs or
  re-executing the code with predicted inputs, we can verify each prediction and
  further enhance the CoTs through multi-turn revision, resulting in CodeI/O++
  and achieving higher performance. Our data and models are available at
  https://github.com/hkust-nlp/CodeIO.
paper_ai_keywords: Chain-of-Thought (CoT) rationales logic flow planning state-space searching decision tree traversal modular decomposition symbolic reasoning scientific reasoning logic reasoning math & numerical reasoning commonsense reasoning multi-turn revision