arXiv:2505.24760 공식 코드 · Papers with Code

REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards

Zafir Stojanovski, Oliver Stanley, Joe Sharratt, Richard Jones, Abdulhakeem Adefioye, Jean Kaddour, Andreas Köpf

제출 2025-05-30

원문 초록

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.

함께 살펴볼 논문

공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.

arXiv:2505.22442v4cs.LG공통 태그 reinforcement-learning, Reinforcement Learning공식 코드 · Papers with Code

Fully Offline Reinforcement Learning

토론

이 논문과 연결된 포럼 글이 아직 없습니다.