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...
Seil Kang, Hangoo Kang, Tarun Suresh et al.
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