title: REASONING GYM: Reasoning Environments for Reinforcement Learning with
    Verifiable Rewards
summary: We introduce Reasoning Gym (RG), a library of reasoning environments for
  reinforcement learning with verifiable rewards. It provides over 100 data
  generators and verifiers spanning multiple domains including algebra,
  arithmetic, computation, cognition, geometry, graph theory, logic, and various
  common games. Its key innovation is the ability to generate virtually infinite
  training data with adjustable complexity, unlike most previous reasoning
  datasets, which are typically fixed. This procedural generation approach allows
  for continuous evaluation across varying difficulty levels. Our experimental
  results demonstrate the efficacy of RG in both evaluating and reinforcement
  learning of reasoning models.
paper_authors._id: 683e6af92139ea008faa74ba
paper_authors.name: Zafir Stojanovski
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paper_authors.fullname: Zafir Stojanovski
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paper_authors.name: Oliver Stanley
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paper_authors.fullname: Oliver Stanley
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paper_authors.user: OllieStanley
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paper_authors.name: Joe Sharratt
paper_authors._id: 683e6af92139ea008faa74bd
paper_authors.name: Richard Jones
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paper_authors.name: Abdulhakeem Adefioye
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paper_authors.fullname: Abdulhakeem Adefioye
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paper_authors.user: kokolamba
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paper_authors.name: Jean Kaddour
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paper_authors.fullname: Jean Kaddour
paper_authors.type: user
paper_authors.user: JeanKaddour
paper_authors._id: 683e6af92139ea008faa74c0
paper_authors.name: Andreas Köpf
paper_title: REASONING GYM: Reasoning Environments for Reinforcement Learning with
    Verifiable Rewards
paper_summary: We introduce Reasoning Gym (RG), a library of reasoning environments for
  reinforcement learning with verifiable rewards. It provides over 100 data
  generators and verifiers spanning multiple domains including algebra,
  arithmetic, computation, cognition, geometry, graph theory, logic, and various
  common games. Its key innovation is the ability to generate virtually infinite
  training data with adjustable complexity, unlike most previous reasoning
  datasets, which are typically fixed. This procedural generation approach allows
  for continuous evaluation across varying difficulty levels. Our experimental
  results demonstrate the efficacy of RG in both evaluating and reinforcement
  learning of reasoning models.
paper_ai_summary: Reasoning Gym provides a library of reasoning environments with verifiable rewards and procedural data generation for reinforcement learning, enabling the evaluation and training of reasoning models at varying difficulty levels.
paper_ai_keywords: reinforcement learning verifiable rewards data generators verifiers procedural generation reasoning models