Preprint Open access
ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning
Language models are moving beyond generating answers to pursuing long-horizon goals in interactive environments. Post-training these agents requires long, heterogeneous trajectories, and synchronous systems leave learner engines idle until rollout and verification finish. To squeeze out these pipeline bubbles, asynchro …