docs: add NCCL troubleshooting notes for multi-GPU training

docs/source/features/multi_gpu.rst

@@ -124,6 +124,36 @@ To train with multiple GPUs, use the following command, where ``--nproc_per_node
 
                     python -m skrl.utils.distributed.jax --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed --ml_framework jax
 
+.. _multi-gpu-nccl-troubleshooting:
+
+Troubleshooting NCCL Errors
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+On some Linux multi-GPU systems, distributed training may fail with
+``CUDA error: an illegal memory access was encountered`` reported by ``ProcessGroupNCCL``
+during or shortly after communicator initialization.
+
+If this occurs, try disabling the NCCL shared-memory transport before launching training:
+
+.. code-block:: shell
+
+    export NCCL_SHM_DISABLE=1
+
+If the issue persists, additional NCCL fallbacks that may help are:
+
+.. code-block:: shell
+
+    export NCCL_IB_DISABLE=1
+    export NCCL_ALGO=Ring
+
+Then relaunch the distributed training command as usual.
+
+.. note::
+
+    These variables are NCCL-level workarounds intended for affected systems. They are not
+    required on all machines, and may change communication behavior or performance depending
+    on the hardware topology.
+
 Multi-Node Training
 -------------------
 

docs/source/refs/troubleshooting.rst

@@ -9,6 +9,14 @@ Tricks and Troubleshooting
     assistance.
 
 
+Troubleshooting distributed training NCCL errors
+------------------------------------------------
+
+On some Linux multi-GPU systems, distributed training may fail with
+``CUDA error: an illegal memory access was encountered`` reported by ``ProcessGroupNCCL``.
+For documented NCCL workarounds, see :ref:`multi-gpu-nccl-troubleshooting`.
+
+
 Debugging physics simulation stability issues
 ---------------------------------------------