diff --git a/nanochat/dataloader.py b/nanochat/dataloader.py
index e95c3af..7086038 100644
--- a/nanochat/dataloader.py
+++ b/nanochat/dataloader.py
@@ -144,66 +144,92 @@ def tokenizing_distributed_data_loader_with_state_bos_bestfit(
     row_capacity = T + 1
     batches = _document_batches(split, resume_state_dict, tokenizer_batch_size)
     bos_token = tokenizer.get_bos_token_id()
-    doc_buffer = []
     pq_idx, rg_idx, epoch = 0, 0, 1
 
+    # Token pool: single tensor holding all buffered tokens
+    # Documents tracked as (start, length) tuples
+    pool = torch.empty(buffer_size * 512, dtype=torch.long)
+    pool_end = 0
+    docs = []  # [(start, length), ...]
+
+    def compact_pool():
+        """Shift active documents to front of pool, reclaiming space."""
+        nonlocal pool_end
+        if not docs:
+            pool_end = 0
+            return
+        write_pos = 0
+        for i, (start, length) in enumerate(docs):
+            if start != write_pos:
+                pool[write_pos:write_pos + length] = pool[start:start + length].clone()
+            docs[i] = (write_pos, length)
+            write_pos += length
+        pool_end = write_pos
+
     def refill_buffer():
-        nonlocal pq_idx, rg_idx, epoch
+        """Retrieve more docs and add them to the pool"""
+        nonlocal pq_idx, rg_idx, epoch, pool, pool_end
         doc_batch, (pq_idx, rg_idx, epoch) = next(batches)
         token_lists = tokenizer.encode(doc_batch, prepend=bos_token, num_threads=tokenizer_threads)
+        # Number of new tokens to store
+        total_new = sum(len(t) for t in token_lists)
+        # If there's not enough space at the end,
+        if pool_end + total_new > pool.size(0):
+            compact_pool() # Try compacting first.
+            # If still not enough,
+            if pool_end + total_new > pool.size(0):
+                # Allocate a new, larger pool.
+                new_size = max(pool.size(0) * 2, pool_end + total_new)
+                new_pool = torch.empty(new_size, dtype=torch.long)
+                new_pool[:pool_end] = pool[:pool_end]
+                pool = new_pool
+        # Write tokens to pool
         for tokens in token_lists:
-            doc_buffer.append(tokens)
+            n = len(tokens)
+            pool[pool_end:pool_end + n] = torch.tensor(tokens, dtype=torch.long)
+            docs.append((pool_end, n))
+            pool_end += n
 
-    # Pre-allocate buffers once: layout is [inputs (B*T) | targets (B*T)]
-    # This gives us contiguous views and a single HtoD transfer
+    # Pre-allocate buffers once
     use_cuda = device == "cuda"
-    cpu_buffer = torch.empty(2 * B * T, dtype=torch.long, pin_memory=use_cuda) # staging area (CPU)
-    gpu_buffer = torch.empty(2 * B * T, dtype=torch.long, device=device) # on-device buffer
-    cpu_inputs = cpu_buffer[:B * T].view(B, T) # a few views into these buffers just for convenience
-    cpu_targets = cpu_buffer[B * T:].view(B, T)
-    inputs = gpu_buffer[:B * T].view(B, T)
-    targets = gpu_buffer[B * T:].view(B, T)
+    row_buffer = torch.empty((B, row_capacity), dtype=torch.long)
+    inputs = torch.empty((B, T), dtype=torch.long, device=device)
+    targets = torch.empty((B, T), dtype=torch.long, device=device)
 
     while True:
-        rows = []
-        for _ in range(B):
-            row = []
-            while len(row) < row_capacity:
+        for row_idx in range(B):
+            col = 0
+            while col < row_capacity:
                 # Ensure buffer has documents
-                while len(doc_buffer) < buffer_size:
+                while len(docs) < buffer_size:
                     refill_buffer()
 
-                remaining = row_capacity - len(row)
+                remaining = row_capacity - col
 
                 # Find largest doc that fits entirely
                 best_idx = -1
                 best_len = 0
-                for i, doc in enumerate(doc_buffer):
-                    doc_len = len(doc)
-                    if doc_len <= remaining and doc_len > best_len:
+                for i, (start, length) in enumerate(docs):
+                    if length <= remaining and length > best_len:
                         best_idx = i
-                        best_len = doc_len
+                        best_len = length
 
                 if best_idx >= 0:
-                    doc = doc_buffer.pop(best_idx)
-                    row.extend(doc)
+                    start, length = docs.pop(best_idx)
+                    row_buffer[row_idx, col:col + length] = pool[start:start + length]
+                    col += length
                 else:
-                    # No doc fits - crop shortest in buffer to fill remaining and minimize waste
-                    shortest_idx = min(range(len(doc_buffer)), key=lambda i: len(doc_buffer[i]))
-                    doc = doc_buffer.pop(shortest_idx)
-                    row.extend(doc[:remaining])
+                    # No doc fits - crop shortest to fill remaining
+                    shortest_idx = min(range(len(docs)), key=lambda i: docs[i][1])
+                    start, length = docs.pop(shortest_idx)
+                    row_buffer[row_idx, col:col + remaining] = pool[start:start + remaining]
+                    col += remaining
 
-            rows.append(row[:row_capacity])
-
-        # Convert rows to tensor and copy slices to pinned buffer (CPU work)
-        row_data = torch.tensor(rows, dtype=torch.long)  # [B, T+1], temporary
-        cpu_inputs.copy_(row_data[:, :-1])
-        cpu_targets.copy_(row_data[:, 1:])
+        # Copy to GPU
+        inputs.copy_(row_buffer[:, :-1], non_blocking=use_cuda)
+        targets.copy_(row_buffer[:, 1:], non_blocking=use_cuda)
 
         state_dict = {"pq_idx": pq_idx, "rg_idx": rg_idx, "epoch": epoch}
-
-        # Single HtoD copy into persistent GPU buffer and yield
-        gpu_buffer.copy_(cpu_buffer, non_blocking=use_cuda)
         yield inputs, targets, state_dict
 
 def tokenizing_distributed_data_loader_bos_bestfit(*args, **kwargs):