Verifiable rewards improve LLM math accuracy

About This Tutorial

RL from verifiable rewards now beats GRPO baselines by a comfortable margin, and the advantage comes from assigning credit at far finer granularity than whole‑response scores. By turning verification into token‑ and subproblem‑level signals, the newest methods extract learning from progress that would otherwise be discarded. Before these works, reinforcement learning for reasoning relied on a single scalar reward per generated answer.