worker_groups.py removes RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES, breaking NCCL on H200/NVSwitch

## Summary

`nemo_rl/distributed/worker_groups.py` (line ~347) unconditionally removes `RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES` from worker environment variables:

```python
# Remove Ray-specific environment variables, let the worker itself set them.
worker_env_vars.pop("RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES", None)
```

This forces Ray to set per-actor `CUDA_VISIBLE_DEVICES` masking (e.g., `CUDA_VISIBLE_DEVICES=3` for the 4th GPU), which triggers **three confirmed NCCL bugs** on H200/NVSwitch (P5en) hardware. The result is that multi-node GRPO training is completely broken on H200.

## NCCL Bugs Triggered by GPU Masking

When Ray masks `CUDA_VISIBLE_DEVICES` to a single GPU per actor, NCCL's internal device indexing diverges from the physical GPU topology. This triggers:

1. **cuMem import penalty** ([NVIDIA/nccl#1749](https://github.com/NVIDIA/nccl/issues/1749)) -- Confirmed by NVIDIA engineer. `p2pMap()` in `src/transport/p2p.cc` iterates over all devices when importing cuMem handles with non-overlapping `CUDA_VISIBLE_DEVICES`. Causes **3,660ms** first-operation penalty (vs 1.5ms with torchrun).

2. **NVLS rank ordering corruption** ([NVIDIA/nccl#1906](https://github.com/NVIDIA/nccl/issues/1906)) -- Confirmed by NVIDIA engineer. `src/transport/nvls.cc` allgather is missing a user rank table when GPU indices are permuted by `CUDA_VISIBLE_DEVICES`. Causes **hang or silent data corruption** on NVSwitch systems. Only affects NVSwitch (H200 P5en), not NVLink-only (H100 P5).

3. **Multi-channel P2P hang at >8M elements** -- Even with `NCCL_CUMEM_ENABLE=0` and `NCCL_NVLS_ENABLE=0`, AllReduce hangs for tensors larger than ~32MB (8-12M float32 elements). This appears to be a separate multi-channel issue triggered by the same GPU masking.

## Benchmarks: Same Hardware, Same NCCL, Different Results

| Method | AllReduce 4KB | AllReduce 933MB | Notes |
|--------|--------------|-----------------|-------|
| **torchrun** (no GPU masking) | **1.5ms** | **1.5ms** | All sizes work perfectly |
| **Ray** (GPU masking forced) | **3,660ms** | **HANGS forever** | 2400x slower, then hangs |

Both tests run on the same P5en.48xlarge nodes (8x H200, NVSwitch), same NCCL 2.27.5, same EFA networking, same container image. The ONLY difference is whether `CUDA_VISIBLE_DEVICES` is masked per-process.

On P5.48xlarge (H100, NVLink PXN, no NVSwitch), Ray with GPU masking works fine -- confirming this is specific to NVSwitch topology.

## Proposed Fix

Make `RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES` **configurable** instead of unconditionally removing it. For example:

```python
# Allow users to opt out of Ray GPU masking (needed for H200/NVSwitch)
if not os.environ.get("NEMO_RL_KEEP_RAY_NOSET_CVD"):
worker_env_vars.pop("RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES", None)
```

Or expose it as a configuration parameter in the worker group config. When `RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1` is preserved, each worker sees all 8 GPUs and must call `torch.cuda.set_device(local_rank)` explicitly -- which NeMo RL workers already do.

## Current Workaround

The only workaround is to patch the installed `worker_groups.py` in the container's venv to comment out the `.pop()` line, which is fragile and breaks on upgrades.

## Environment

- **Hardware**: 4x P5en.48xlarge (8x H200 per node, NVSwitch)
- **NCCL**: 2.27.5 with aws-ofi-nccl 1.18.0
- **NeMo RL**: Latest main branch
- **Ray**: 2.44.1
- **Network**: EFA with Libfabric 2.4

## Related Issues

- ray-project/ray#61073 -- NCCL AllReduce 2400x slower via Ray on H200 (upstream Ray issue)
- #1961 -- Multi-node GRPO fails on P5en (broader tracking issue)
- NVIDIA/nccl#1749 -- cuMem import penalty (confirmed by NVIDIA)
- NVIDIA/nccl#1906 -- NVLS rank ordering (confirmed by NVIDIA)