Training a model from reward signals on its own generated trajectories rather than from fixed labels; the core of agent-era post-training.
Tensions
Luo Fuli frames "how to do RL scaling on agents" as the competitive crux of 2026. The infra implication is a shift from a rollout-inference-engine-centric system (long chains of thought ending in an answer) to an agent-centric system where the model-agent coupling is the hard problem, demanding new and more agile RL infra. Yao Shunyu adds the provocation that pre-training is also a kind of RL, blurring the line between the two stages.