We’re entering the age of large-scale synthetic data

This post introduces Sim2Reason, a novel system accepted at ICML 2026 that demonstrates the efficacy of large-scale synthetic data for training AI models. Sim2Reason uses physics simulators (MuJoCo) to procedurally generate diverse physical scenarios, from which verified question-answer pairs are automatically constructed across numeric, reverse, and symbolic modes. Models trained purely on Sim2Reason data showed significant zero-shot performance improvements (5-10 percentage points on IPhO mechanics problems, +17.9% on JEEBench for 32B models) and generalized to out-of-domain tasks. The system highlights that aligning synthetic data generation processes leads to higher-signal distributions than generic internet corpora, emphasizing 'better data' over 'more data'. This work contributes to the growing field of synthetic data generation by providing a concrete example of a simulation-driven approach that surpasses traditional methods.