Weight transfer for RL post-training in under 2 seconds

A system for asynchronous reinforcement learning fine-tuning of the trillion-parameter Kimi-K2 model achieved 1.3-second cross-machine weight updates from 256 BF16 training GPUs to 128 FP8 inference GPUs. It uses one-sided RDMA WRITE operations, allowing training nodes to write directly into inference GPU memory without destination-side notifications, CPU control logic, extra copies, or modifications to the inference engine. A controller gathers metadata once, creates and distributes a static GPU-to-GPU transfer schedule, and signals training GPUs to replay it after each training step. The implementation distributes parameter-transfer tasks across disjoint DeviceMesh groups and pipelines CPU-to-GPU copying, parameter reconstruction and quantization, RDMA transfers, and synchronization to overlap hardware resources while controlling temporary GPU memory through a configurable watermark. By replacing rank-0 gathering and scattering with parallel point-to-point transfers, avoiding repeated scheduling and RPC overhead, and separating transfer functions into testable components, the approach aims to provide faster, more maintainable large-scale model updates.

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