ThunderSyncRL Speeds Up Synchronous Agentic RL by Up to 1.9x

Researchers released ThunderSyncRL, a project that optimizes synchronous GRPO-style agentic RL training by overlapping gradient computation with rollout sampling, achieving up to 1.9x speedup.

2026-10-08 ~ 2026-10-10 · 2 related posts

• ThunderSyncRL: Sync agentic RL gets up to 1.9x faster by overlapping gradients with rollouts — YouJiacheng · 2026-10-08
• ThunderSyncRL speeds up synchronous agentic RL by up to 1.9x with zero policy staleness — StanfordAILab · 2026-10-10