diff --git a/TPU_ISSUES_RECORD.md b/TPU_ISSUES_RECORD.md
index ab09f80..f0db3a5 100644
--- a/TPU_ISSUES_RECORD.md
+++ b/TPU_ISSUES_RECORD.md
@@ -74,10 +74,65 @@ self.accelerator = Accelerator(
 - TPU v5e-8: 8 cores × 16GB = 128GB total
 - Fewer cores = LESS total memory (not more per core)
 
+## Latest Status (2025-10-12)
+
+### After DataLoaderConfiguration Fix
+✅ **even_batches Error RESOLVED** - No more `ValueError: You need to use 'even_batches=False'`
+
+❌ **NEW ERROR**: `TypeError: 'NoneType' object is not iterable`
+```
+File "/usr/local/lib/python3.12/site-packages/accelerate/data_loader.py", line 221, in _iter_with_no_split
+    for idx, batch in enumerate(self.batch_sampler):
+                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+TypeError: 'NoneType' object is not iterable
+```
+
+**Root Cause**: `batch_sampler` becomes `None` when our DataLoader has `batch_size=None`
+
+### Current Investigation
+- The issue is in Accelerate's data_loader.py line 221
+- Our custom dataset returns full batches, so we use `batch_size=None` in DataLoader
+- But Accelerate expects a proper batch_sampler when iterating
+- This is a fundamental incompatibility between our batching approach and Accelerate's expectations
+
+## FINAL SOLUTION ✅
+
+### Problem Resolution
+1. ~~even_batches Error~~ ✅ RESOLVED with DataLoaderConfiguration
+2. ~~batch_sampler None Error~~ ✅ RESOLVED with custom collate_fn
+
+### Final Implementation
+```python
+# In rnn_trainer.py prepare_dataloaders()
+
+# Custom collate function that handles pre-batched data from our dataset
+def collate_fn(batch):
+    # Our dataset returns full batches, so batch will be a list of single batch dict
+    # Extract the first (and only) element since our dataset.__getitem__() returns a full batch
+    if len(batch) == 1 and isinstance(batch[0], dict):
+        return batch[0]
+    else:
+        # Fallback for unexpected batch structure
+        return batch
+
+# DataLoader configuration compatible with Accelerate
+self.train_loader = DataLoader(
+    self.train_dataset,
+    batch_size = 1,  # Use batch_size=1 since dataset returns full batches
+    shuffle = shuffle_setting,
+    num_workers = workers_setting,
+    pin_memory = True,
+    collate_fn = collate_fn
+)
+```
+
+**Key Insight**: Our dataset's `__getitem__()` returns complete batches, but Accelerate expects individual samples. The solution is to use `batch_size=1` and a custom `collate_fn` that unwraps the pre-batched data.
+
 ## Next Steps
-1. Implement correct even_batches=False in accelerator.prepare()
-2. Test TPU training without overengineering
-3. Verify memory usage with 8 cores configuration
+1. ~~Implement even_batches=False~~ ✅ DONE
+2. ~~Fix batch_sampler None issue~~ ✅ DONE
+3. Test TPU training with complete solution
+4. Integrate final solution into CLAUDE.md
 
 ## Lessons Learned
 - Don't overcomplicate TPU conversion - it should be straightforward
diff --git a/model_training_nnn/rnn_trainer.py b/model_training_nnn/rnn_trainer.py
index 5db7a28..8d4c77c 100644
--- a/model_training_nnn/rnn_trainer.py
+++ b/model_training_nnn/rnn_trainer.py
@@ -189,13 +189,24 @@ class BrainToTextDecoder_Trainer:
             random_seed = self.args['dataset']['seed'],
             feature_subset = feature_subset
             )
-        # Standard DataLoader configuration - let Accelerator handle device-specific optimizations
+        # Custom collate function that handles pre-batched data from our dataset
+        def collate_fn(batch):
+            # Our dataset returns full batches, so batch will be a list of single batch dict
+            # Extract the first (and only) element since our dataset.__getitem__() returns a full batch
+            if len(batch) == 1 and isinstance(batch[0], dict):
+                return batch[0]
+            else:
+                # Fallback for unexpected batch structure
+                return batch
+
+        # DataLoader configuration compatible with Accelerate
         self.train_loader = DataLoader(
             self.train_dataset,
-            batch_size = None, # Dataset.__getitem__() already returns batches
+            batch_size = 1,  # Use batch_size=1 since dataset returns full batches
             shuffle = self.args['dataset']['loader_shuffle'],
             num_workers = self.args['dataset']['num_dataloader_workers'],
-            pin_memory = True
+            pin_memory = True,
+            collate_fn = collate_fn
         )
 
         # val dataset and dataloader
@@ -209,13 +220,14 @@ class BrainToTextDecoder_Trainer:
             random_seed = self.args['dataset']['seed'],
             feature_subset = feature_subset   
             )
-        # Standard validation DataLoader configuration
+        # Validation DataLoader with same collate function
         self.val_loader = DataLoader(
             self.val_dataset,
-            batch_size = None, # Dataset.__getitem__() already returns batches
+            batch_size = 1,  # Use batch_size=1 since dataset returns full batches
             shuffle = False,
             num_workers = 0,  # Keep validation dataloader single-threaded for consistency
-            pin_memory = True
+            pin_memory = True,
+            collate_fn = collate_fn  # Use same collate function
         )
 
         self.logger.info("Successfully initialized datasets")