Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning
Authors: name: Valentin Lacombe, name: Valentin Quesnel, name: Damien Sileo

We introduce Reasoning Core, a new scalable environment for Reinforcement
Learning with Verifiable Rewards (RLVR), designed to advance foundational
symbolic reasoning in Large Language Models (LLMs). Unlike existing benchmarks
that focus on games or isolated puzzles, Reasoning Core procedurally generates
problems across core formal domains, including PDDL planning, first-order
logic, context-free grammar parsing, causal reasoning, and system equation
solving. The environment is built on key design principles of high-generality
problem distributions, verification via external tools, and continuous
difficulty control, which together provide a virtually infinite supply of novel
training instances. Initial zero-shot evaluations with frontier LLMs confirm
the difficulty of Reasoning Core's tasks, positioning it as a promising
resource to improve the reasoning capabilities of future models.