mlm-bridge-training

You can compare Megatron-LM MLM and Bridge training losses by running provided recipes side-by-side on multi-GPU setups. This executes both workflows to generate reproducible experiment outputs for direct loss comparison and correlation testing.

Yes, you can use mock data for Megatron-LM distributed training experiments. The recipes support mock data inputs alongside real data, allowing you to verify configuration translations and test training consistency without preparing a full dataset.

Bridge training configuration translation in Megatron-LM is the documented process of mapping parameters between MLM pretraining and Bridge training recipes. It ensures consistent, reproducible training workflows across different experimental configurations.

Yes, correlation testing verifies consistency between MLM and Bridge training runs by comparing their losses and configuration translations. This validates that training results remain reproducible across different multi-GPU environments and recipe setups.

To set up multi-GPU environments for Megatron-LM Bridge training, follow the explicit environment setup steps provided in the recipes. These include configuring CLI arguments and distributed training settings to execute scalable benchmarks.