REASONING GYM: Reasoning Environments for Reinforcement Learning with
  Verifiable Rewards
Authors: name: Zafir Stojanovski, name: Oliver Stanley, name: Joe Sharratt, name: Richard Jones, name: Abdulhakeem Adefioye, name: Jean Kaddour, name: Andreas Köpf

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.