#math benchmarks

SRL converts expert trajectories into per-step rewarded actions and lets models produce private reasoning spans before each action, giving dense learning signals that boost 7B open models on hard math and coding tasks

SwiReasoning alternates between silent latent reasoning and explicit chain-of-thought based on next-token entropy, improving accuracy and dramatically cutting token usage on math and STEM benchmarks.