docs: fix typos and errors in training-backends.md (#2287)

Signed-off-by: Terry Kong <terryk@nvidia.com>

Author: terrykong

docs/design-docs/training-backends.md

@@ -35,13 +35,9 @@ _Note_: When using Megatron, the optimizer and learning rate schedule are config
 ### DTensor Backend
 To enable DTensor (FSDP2) training:
 
-1. Set `policy.dtensor_config.enabled=True`.
+1. Set `policy.dtensor_cfg.enabled=True`.
 2. Refer to [examples/configs/grpo_math_1B.yaml](../../examples/configs/grpo_math_1B.yaml) for a configuration example.
 
-## Backend Priority
-
-**Megatron takes precedence over DTensor.** If both backends are enabled simultaneously (`policy.megatron_cfg.enabled=True` and `policy.dtensor_config.enabled=True`), the Megatron backend will be used.
-
 ## Configuration Examples
 
 For comprehensive examples of each algorithm and backend, see the [examples/configs/recipes/llm](https://github.com/NVIDIA-NeMo/RL/tree/main/examples/configs/recipes/llm) folder. This directory contains ready-to-use configurations for various supported combinations.
@@ -72,7 +68,7 @@ export HF_HOME="/shared/nfs/huggingface"
 
 ### Best Practices ###
 
-- **Mount in checkpoint directory**: If you are using Docker, make sure the Megatron checkpoint path is covered by `-v`/`--mount`. Similarly, if you are using SLURM+pyxis, ensure `--container-mounts` includes this path.
+- **Mount the checkpoint directory**: If you are using Docker, make sure the Megatron checkpoint path is covered by `-v`/`--mount`. Similarly, if you are using SLURM+pyxis, ensure `--container-mounts` includes this path.
 - **Use shared storage**: Ensure the checkpoint directory is accessible from all nodes (e.g., NFS, shared filesystem).
 - **Prefer HF_HOME**: If you already have `HF_HOME` mounted across nodes, this reduces the number of environment variables to manage.
 - **Sufficient space**: Ensure adequate disk space for the converted model checkpoints.