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Model: deepseek-reasoner

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Messages: 30

UserStep 0

Task: llm-pretrain-residual

LLM Pretraining: Residual Connection Strategy

Research Question

Improve the residual connection strategy in a GPT-style language model. The current architecture uses standard Pre-LN residual connections (x + sublayer(x)) in each transformer block. Your task is to redesign how information flows through the residual stream across layers to achieve lower validation loss.

Background

Standard Residual Connections

The default GPT architecture uses simple additive residual connections in each block:

x = x + self.attn(self.ln_1(x))   # attention sublayer
x = x + self.mlp(self.ln_2(x))    # MLP sublayer

While effective, this fixed accumulation pattern may not be optimal for deep networks. The residual stream is the primary information highway through the model, and its design critically affects gradient flow, feature reuse, and training dynamics.

Research Directions

Several recent works have proposed improvements to residual connections:

Per-layer residual scaling: Learnable scalars that modulate the residual stream at each layer (inspired by modded-nanogpt, ReZero, SkipInit).
Initial embedding blending: Blending the initial token embedding back at each layer to preserve token identity (x0 residual connections).
Hyper-Connections: Maintaining m parallel residual streams with learned transition matrices for richer information flow across layers (Zhu et al., 2025).
Attention Residuals: Using softmax attention over all previous layer outputs to dynamically select which representations to combine (Kimi Team, 2026).

What You Can Modify

Block Class (lines 88-99)

The Block class defines per-block residual behavior. You can change how attention and MLP outputs are combined with the residual stream within each block.

Residual Stream Parameters (lines 128-130)

Add custom parameters to GPT.__init__ for your residual connection strategy (e.g., per-layer scalars, transition matrices, query vectors).

Block Loop in GPT.forward (lines 162-164)

The main loop that iterates through transformer blocks. You can modify how blocks are called and how their outputs are accumulated (e.g., multi-stream processing, attention over layer outputs).

Optimizer Configuration (lines 175-192)

The configure_optimizers method. If you add new parameters, you may want to assign them to appropriate optimizer groups with custom learning rates and weight decay.

Training Hyperparameters (line 251)

The CONFIG_OVERRIDES dictionary for adjusting learning rate, weight decay, etc.

Note: The CausalSelfAttention, MLP, LayerNorm, and GPTConfig classes are fixed. The Block.forward signature must accept x and return a tensor of the same shape. The GPT.forward must accept (idx, targets=None) and return (logits, loss).

Evaluation

Primary metric: Validation loss (val_loss, lower is better)
Secondary metrics: Perplexity on WikiText-2 and LAM

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 88,
5  "end_line": 99,
6  "content": "class Block(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)\n        self.attn = CausalSelfAttention(config)\n        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)\n        self.mlp = MLP(config)\n        # Dynamic residual gates\n        # Small MLP that takes reduced representation of pre-norm activations\n        # and outputs a gate scalar for the residual\n        gate_dim = config.n_embd // 16  # reduced dimension\n        self.attn_gate_net = nn.Sequential(\n            nn.Linear(config.n_embd, gate_dim, bias=False),\n            nn.GELU(),\n            nn.Linear(gate_dim, 1, bias=False),\n            nn.Sigmoid()\n        )\n        self.mlp_gate_net = nn.Sequential(\n            nn.Linear(config.n_embd, gate_dim, bias=False),\n            nn.GELU(),\n            nn.Linear(gate_dim, 1, bias=False),\n            nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        # Attention residual with dynamic gate\n        x_norm = self.ln_1(x)\n        attn_out = self.attn(x_norm)\n        attn_gate = self.attn_gate_net(x_norm)  # (B, T, 1)\n        x = x + attn_gate * attn_out\n        # MLP residual with dynamic gate\n        x_norm2 = self.ln_2(x)\n        mlp_out = self.mlp(x_norm2)\n        mlp_gate = self.mlp_gate_net(x_norm2)  # (B, T, 1)\n        x = x + mlp_gate * mlp_out\n        return x"
7}

Tool ResultStep 1

OK: Replaced lines 88..99 with 36 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–123, 152–154, 186–188, 199–216, 275–275.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–123, 152–154, 186–188, 199–216, 275–275 | total: 465 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (30 more lines) ...
   121          mlp_gate = self.mlp_gate_net(x_norm2)  # (B, T, 1)
   122          x = x + mlp_gate * mlp_out
   123          return x
...
   152          # ── Residual stream parameters ──
   153          # (default: none — vanilla residual x + sublayer(x) is in Block.forward)
   154          # Add custom residual parameters here if needed.
...
   186          # ── Residual stream: iterate through transformer blocks ──
   187          for block in self.transformer.h:
   188              x = block(x)
...
   199      def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
   200

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=1000, train_loss=3.5382, val_loss=3.3044
TRAIN_METRICS: step=2000, train_loss=3.1197, val_loss=2.8466
TRAIN_METRICS: step=3000, train_loss=2.9487, val_loss=2.6748
TRAIN_METRICS: step=4000, train_loss=2.8639, val_loss=2.5732
TRAIN_METRICS: step=5000, train_loss=2.8075, val_loss=2.5186

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.5000, hellaswag_lm-eval-345m=33.2800, piqa_lm-eval-345m=64.3600, winogrande_lm-eval-345m=51.7800

[Leaderboard] Results saved: {'arc_easy_lm-eval-345m': 54.5, 'hellaswag_lm-eval-345m': 33.28, 'piqa_lm-eval-345m': 64.36, 'winogrande_lm-eval-345m': 51.78, 'elapsed_lm-eval-345m': 584}

AssistantStep 3undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 3

ERROR: Nothing to undo

AssistantStep 4edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 152,
5  "end_line": 154,
6  "content": "        # ── Adaptive Layer Scaling (ALS) parameters ──\n        # Base scaling per layer for attention and MLP residuals\n        self.attn_base = nn.Parameter(torch.ones(config.n_layer))\n        self.mlp_base = nn.Parameter(torch.ones(config.n_layer))\n        # Modulation strength\n        self.beta = nn.Parameter(torch.zeros(1))\n        # Optional: learnable per-layer decay for residual memory (future)\n        # self.lambda_ = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.apply(self._init_weights)"
7}

Tool ResultStep 4

ERROR: Lines 152..154 exceed the editable range. You may only edit lines 88–123.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–123 | total: 465 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (30 more lines) ...
   121          mlp_gate = self.mlp_gate_net(x_norm2)  # (B, T, 1)
   122          x = x + mlp_gate * mlp_out
   123          return x

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 88,
5  "end_line": 123,
6  "content": "class Block(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)\n        self.attn = CausalSelfAttention(config)\n        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)\n        self.mlp = MLP(config)\n\n    def forward(self, x):\n        x = x + self.attn(self.ln_1(x))\n        x = x + self.mlp(self.ln_2(x))\n        return x"
7}

Tool ResultStep 5

OK: Replaced lines 88..123 with 12 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–99.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–99 | total: 441 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (6 more lines) ...
    97          x = x + self.attn(self.ln_1(x))
    98          x = x + self.mlp(self.ln_2(x))
    99          return x

AssistantStep 6edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 128,
5  "end_line": 130,
6  "content": "        # ── Adaptive Layer Scaling (ALS) parameters ──\n        # Base scaling per layer for attention and MLP residuals\n        self.attn_base = nn.Parameter(torch.ones(config.n_layer))\n        self.mlp_base = nn.Parameter(torch.ones(config.n_layer))\n        # Modulation strength\n        self.beta = nn.Parameter(torch.zeros(1))\n        self.apply(self._init_weights)"
7}

Tool ResultStep 6

ERROR: Lines 128..130 exceed the editable range. You may only edit lines 88–99.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–99 | total: 441 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (6 more lines) ...
    97          x = x + self.attn(self.ln_1(x))
    98          x = x + self.mlp(self.ln_2(x))
    99          return x

AssistantStep 7edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 162,
5  "end_line": 164,
6  "content": "        # ── Shared Dynamic Gates residual ──\n        # Parameters for gate network (added in __init__)\n        # For now placeholder: we need to define gate network elsewhere\n        # We'll implement later\n        for block in self.transformer.h:\n            x = block(x)"
7}

Tool ResultStep 7

ERROR: Lines 162..164 exceed the editable range. You may only edit lines 88–99.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–99 | total: 441 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (6 more lines) ...
    97          x = x + self.attn(self.ln_1(x))
    98          x = x + self.mlp(self.ln_2(x))
    99          return x

AssistantStep 8test