Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them.:

The possibilities are endless.

Plugging existing agents into RL training without rebuilding them is huge. Lowering that barrier could make agent improvement way more accessible for builders 🔥

RL gives agents a way to learn from execution. I think there’s another question worth asking: what happens to the useful knowledge inside those executions after the model gets updated? Not every lesson should disappear into a weight update. Some should remain traceable and reusable by the next agent. That distinction is pretty important.

The more interesting implication here is that RL for agents flips the bottleneck — the environment becomes the constraint, not model capacity. Flaky tools and slow APIs probably shape the reward signal more than the policy itself.