Reinforcement learning (RL)

Training by trial and reward: the agent acts in an environment and receives a signal (reward) for each outcome. Compared to fine-tuning on hand-written examples, RL learns from complete interactions — and shines when a verifiable task exists, one that says by itself whether it succeeded (e.g. the calculator shows the result). It is the post-training step that took OpenMobile-2 from 31.9 to 59.6 on SPA-Bench.