N-gated Hacker NewsJun 2, 2025
Ah, yes, the "ReasoningGym" 🤖💪—because what every #AI needs is a #workout, complete with verifiable protein shakes... I mean, rewards. Just when you thought your digital assistant couldn't get any more condescending, enter stage left: a whole new level of machine self-righteousness sponsored by the Simons Foundation. 🙄🎉
https://arxiv.org/abs/2505.24760 #ReasoningGym #SimonsFoundation #MachineLearning #SelfRighteousness #HackerNews #ngated
REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards

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.

arXiv.org
Hacker NewsJun 2, 2025

ReasoningGym: Reasoning Environments for RL with Verifiable Rewards

https://arxiv.org/abs/2505.24760

#HackerNews #ReasoningGym #ReinforcementLearning #VerifiableRewards #AIResearch #MachineLearning

REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards

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.

arXiv.org