RL-Kernel v0.1.0 is now available. 🎉 Bitwise train–inference consistency across parallel configurations and heterogeneous execution engines for RL post-training. With Qwen3-8B on a full vime pipeline, RL-Kernel recorded zero train–rollout LogP mismatches across all 200 training steps. 1,129 commits from 29 contributors. Highlights: 🎯 Strict numerical alignment across rollout and training engines ⚡ 68.7% higher rollout throughput and 8.2% lower end-to-end step time on NVIDIA H100 🔶 AMD Instinct MI300X: zero mismatches across 9,400,614 active-token LogP elements from 1,600 samples 🔁 End-to-end integration with vime, vLLM rollout, and Megatron-LM training 🧩 Deterministic Attention, dense FFN, LogP, reduction, and collective operators 🌐 NVIDIA CUDA and AMD ROCm support, with initial Ascend integration and MUSA adaptation underway GitHub 👇 github.com/RL-Align/RL-Kerne…