Online RL from Real Interaction Trajectories Gains Traction

Practitioners argue that online reinforcement learning built on real interaction trajectories is the most viable path for solving complex tasks, while personal continual learning currently relies on careful harness engineering and long-horizon RL across memory and multi-agent settings.

2026-09-19 ~ 2026-09-19 · 2 related posts