RL-Kernel
An open-source project focused on numerical alignment of reinforcement learning training for large language models. By standardizing reduction order, intermediate precision, rounding points, and mathematical primitives, it achieves bit-for-bit consistency of log-probabilities between training and inference frameworks.
Timeline (last 90 days)
The RL-Kernel team integrated RL-Align/RL-Kernel with relevant components of vLLM, achieving bit-for-bit matching of training and inference logprobs on the AMD ROCm platform.
Mathematical primitives from different hardware vendors (especially hardware implementations of transcendental functions exp/log) are permitted to differ in last-bit precision under compliance standards, which is why RL-Kernel needs to separately align at the mathematical primitive level.
RL-Kernel's strict path achieves bit-for-bit alignment by unifying four key computation aspects on both the training and inference sides—reduction order, intermediate precision, rounding points, and mathematical primitives.
All Facts (3)
Mathematical primitives from different hardware vendors (especially hardware implementations of transcendental functions exp/log) are permitted to differ in last-bit precision under compliance standards, which is why RL-Kernel needs to separately align at the mathematical primitive level.
50%UnverifiedRL-Kernel's strict path achieves bit-for-bit alignment by unifying four key computation aspects on both the training and inference sides—reduction order, intermediate precision, rounding points, and mathematical primitives.
50%UnverifiedThe RL-Kernel team integrated RL-Align/RL-Kernel with relevant components of vLLM, achieving bit-for-bit matching of training and inference logprobs on the AMD ROCm platform.
50%