# Score Centering

In large-model reinforcement learning, samplers have usually run on the same accelerator generation and precision as the trainer to keep the two numerically close. Xiaomi's MiMo team instead spread samplers for Qwen3.6-35B-A3B agentic coding across six accelerators, B200, B300, H100, H200, A100 and RTX PRO 6000, each with its own NVFP4, FP8 or BF16 weights and kernels, while the trainer updates in BF16 on B200. With off-policy correction and autoscaling serverless GPUs, the mixed samplers match homogeneous samplers on SWE-bench Pro V2 and cut sampling cost per update by about 15% for most of training.

In large-model reinforcement learning, samplers have usually run on the same accelerator generation and precision as the trainer to keep the two numerically close. Xiaomi's MiMo team instead spread samplers for Qwen3.6-35B-A3B agentic coding across six accelerators, B200, B300, H100, H200, A100 and RTX PRO 6000, each with its own NVFP4, FP8 or BF16 weights and kernels, while the trainer updates in BF16 on B200. With off-policy correction and autoscaling serverless GPUs, the mixed samplers match homogeneous samplers on SWE-bench Pro V2 and cut sampling cost per update by about 15% for most of training.