In the news
Zero Mismatch, Simple Async RL: REINFORCE Zero, DeepSeek-V4.1 and Kimi K3.1
vLLM · Ao Shen (Inferact), Jian Hu (NVIDIA Research), Yan Bai (NVIDIA DevTech), Shekhar Pandey (AMD), and the REINFORCE Zero authors · Published · 3 min read
In 30 seconds
- What happened
- REINFORCE Zero enables stable asynchronous reinforcement learning by aligning computational kernels between rollout and training, then applying unclipped importance sampling to handle weight lag.
- Why it matters
- Engineers optimizing large language model training care when they need faster asynchronous RL that maintains training stability without sample rejection or gradient clipping.
- Watch out
- Kernel alignment adds 6.8% computational overhead compared to standard kernels, and requires model-specific engineering work for each new architecture or hardware platform.
- kernel
- deepseek
- kimi
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