ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning

Excerpt

arXiv:2610.05935v1 Announce Type: cross Abstract: 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, asynchronous training overlaps rollout and learning across updates, but comes at the cost of policy staleness. We introduce ThunderSyncRL,