Agent Conversation: gpt-5.4

Model: gpt-5.4

Total steps: 13

Messages: 28

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\n    def forward(self, x):\n        # Return vanilla block output; cross-layer residual routing is handled in GPT.forward.\n        x = x + self.attn(self.ln_1(x))\n        x = x + self.mlp(self.ln_2(x))\n        return x"
7}

Tool ResultStep 1

OK: Replaced lines 88..99 with 13 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–100, 129–131, 163–165, 176–193, 252–252.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–100, 129–131, 163–165, 176–193, 252–252 | total: 442 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (7 more lines) ...
    98          x = x + self.attn(self.ln_1(x))
    99          x = x + self.mlp(self.ln_2(x))
   100          return x
...
   129          # ── Residual stream parameters ──
   130          # (default: none — vanilla residual x + sublayer(x) is in Block.forward)
   131          # Add custom residual parameters here if needed.
...
   163          # ── Residual stream: iterate through transformer blocks ──
   164          for block in self.transformer.h:
   165              x = block(x)
...
   176      def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
   177          param_di

AssistantStep 2edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 129,
5  "end_line": 131,
6  "content": "        # ── ARX: Anchored Residual Mixture ──\n        # Each layer mixes three information sources: previous state x,\n        # current block update delta, and the initial embedding stream x0.\n        # A learned depth-specific gate is normalized with softmax to keep\n        # routing stable while still allowing dynamic residual reallocation.\n        self.arx_logits = nn.Parameter(torch.zeros(config.n_layer, 3))\n        self.arx_temp = 1.0"
7}

Tool ResultStep 2

OK: Replaced lines 129..131 with 7 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–100, 129–135, 167–169, 180–197, 256–256.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–100, 129–135, 167–169, 180–197, 256–256 | total: 446 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (7 more lines) ...
    98          x = x + self.attn(self.ln_1(x))
    99          x = x + self.mlp(self.ln_2(x))
   100          return x
...
   129          # ── ARX: Anchored Residual Mixture ──
   130          # Each layer mixes three information sources: previous state x,
   131          # current block update delta, and the initial embedding stream x0.
   132          # A learned depth-specific gate is normalized with softmax to keep
   133          # routing stable while still allowing dynamic residual reallocation.
   134          self.arx_logits = nn.Parameter(torch.zeros(config.n_layer, 3))
   135          self.a

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 167,
5  "end_line": 169,
6  "content": "        # ── ARX residual stream: softmax-normalized anchored mixture ──\n        x0 = x\n        for i, block in enumerate(self.transformer.h):\n            block_out = block(x)\n            delta = block_out - x\n            mix = F.softmax(self.arx_logits[i] / self.arx_temp, dim=0)\n            x = mix[0] * x + mix[1] * delta + mix[2] * x0"
7}

Tool ResultStep 3

OK: Replaced lines 167..169 with 7 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–100, 129–135, 167–173, 184–201, 260–260.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–100, 129–135, 167–173, 184–201, 260–260 | total: 450 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (7 more lines) ...
    98          x = x + self.attn(self.ln_1(x))
    99          x = x + self.mlp(self.ln_2(x))
   100          return x
...
   129          # ── ARX: Anchored Residual Mixture ──
   130          # Each layer mixes three information sources: previous state x,
   131          # current block update delta, and the initial embedding stream x0.
   132          # A learned depth-specific gate is normalized with softmax to keep
   133          # routing stable while still allowing dynamic residual reallocation.
   134          self.arx_logits = nn.Parameter(torch.zeros(config.n_layer, 3))
   135          self.a

AssistantStep 4edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 184,
5  "end_line": 201,
6  "content": "    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):\n        arx_ids = {id(self.arx_logits)}\n        param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}\n        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2 and id(p) not in arx_ids]\n        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2 and id(p) not in arx_ids]\n        arx_params = [p for n, p in param_dict.items() if id(p) in arx_ids]\n        optim_groups = [\n            {'params': decay_params, 'weight_decay': weight_decay},\n            {'params': nodecay_params, 'weight_decay': 0.0},\n            {'params': arx_params, 'weight_decay': 0.0},\n        ]\n        num_decay_params = sum(p.numel() for p in decay_params)\n        num_nodecay_params = sum(p.numel() for p in nodecay_params)\n        num_arx_params = sum(p.numel() for p in arx_params)\n        print(f\"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters\")\n        print(f\"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters\")\n        print(f\"num ARX parameter tensors: {len(arx_params)}, with {num_arx_params:,} parameters\")\n        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters\n        use_fused = fused_available and device_type == 'cuda'\n        extra_args = dict(fused=True) if use_fused else dict()\n        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)\n        print(f\"using fused AdamW: {use_fused}\")\n        return optimizer"
7}

Tool ResultStep 4

OK: Replaced lines 184..201 with 23 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–100, 129–135, 167–173, 184–206, 265–265.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–100, 129–135, 167–173, 184–206, 265–265 | total: 455 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (7 more lines) ...
    98          x = x + self.attn(self.ln_1(x))
    99          x = x + self.mlp(self.ln_2(x))
   100          return x
...
   129          # ── ARX: Anchored Residual Mixture ──
   130          # Each layer mixes three information sources: previous state x,
   131          # current block update delta, and the initial embedding stream x0.
   132          # A learned depth-specific gate is normalized with softmax to keep
   133          # routing stable while still allowing dynamic residual reallocation.
   134          self.arx_logits = nn.Parameter(torch.zeros(config.n_layer, 3))
   135          self.

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 265,
5  "end_line": 265,
6  "content": "    CONFIG_OVERRIDES = {}"
7}

Tool ResultStep 5

OK: Replaced lines 265..265 with 1 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–100, 129–135, 167–173, 184–206, 265–265.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–100, 129–135, 167–173, 184–206, 265–265 | total: 455 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (7 more lines) ...
    98          x = x + self.attn(self.ln_1(x))
    99          x = x + self.mlp(self.ln_2(x))
   100          return x
...
   129          # ── ARX: Anchored Residual Mixture ──
   130          # Each layer mixes three information sources: previous state x,
   131          # current block update delta, and the initial embedding stream x0.
   132          # A learned depth-specific gate is normalized with softmax to keep
   133          # routing stable while still allowing dynamic residual reallocation.
   134          self.arx_logits = nn.Parameter(torch.zeros(config.n_layer, 3))
   135          self.a

AssistantStep 6test

Tool ResultStep 6

[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=9000, train_loss=3.8976, val_loss=3.6259
TRAIN_METRICS: step=10000, train_loss=3.8540, val_loss=3.5959
TRAIN_METRICS: step=11000, train_loss=6.4850, val_loss=6.3646
TRAIN_METRICS: step=12000, train_loss=3.8737, val_loss=3.6065
TRAIN_METRICS: step=13000, train_loss=3.9084, val_loss=3.6579
Final metrics (gpt-345m): val_loss_gpt-345m=3.5848, wikitext2_ppl_gpt-345m=315.1900, lambada_ppl_gpt-345m=267.5300

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=40.0700, hellaswag_lm-eval-345m=26.2200, piqa_lm-eval-345m=57.1800, winogrande_lm-eval-345m=51.2200

[Leaderboard] Results saved: {'val_loss_gpt-345m': 3.5848, 'wikitext2_ppl_gpt-345m': 315.19, 'lambada_ppl_gpt-345m': 267.53, 'elapsed_gpt-345m': 21133, 'arc_easy_lm-eval-345m': 40.07, 'hellaswag_lm-eval-345m': 26.22,

AssistantStep 7edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 129,
5  "end_line": 135,
6  "content": "        # ── DARC: Depth-Adaptive Residual Competition ──\n        # Layer i chooses among carry x, local delta, and anchor x0 using\n        # content-aware gates from a tiny shared controller. The controller sees\n        # pooled statistics of x and delta, producing token-independent but\n        # input-adaptive routing that can increase update strength only when a\n        # layer has a confident transform to contribute.\n        self.darc_controller = nn.Linear(2, 3, bias=True)\n        self.darc_depth_bias = nn.Parameter(torch.zeros(config.n_layer, 3))"
7}

Tool ResultStep 7

OK: Replaced lines 129..135 with 8 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–100, 129–136, 168–174, 185–207, 266–266.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–100, 129–136, 168–174, 185–207, 266–266 | total: 456 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (7 more lines) ...
    98          x = x + self.attn(self.ln_1(x))
    99          x = x + self.mlp(self.ln_2(x))
   100          return x
...
   129          # ── DARC: Depth-Adaptive Residual Competition ──
   130          # Layer i chooses among carry x, local delta, and anchor x0 using
   131          # content-aware gates from a tiny shared controller. The controller sees
   132          # pooled statistics of x and delta, producing token-independent but
   133          # input-adaptive routing that can increase update strength only when a
   134          # layer has a confident transform to contribute.
   135

AssistantStep 8edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 168,
5  "end_line": 174,
6  "content": "        # ── DARC residual stream: content-adaptive depth-wise routing ──\n        x0 = x\n        for i, block in enumerate(self.transformer.h):\n            block_out = block(x)\n            delta = block_out - x\n            stats = torch.stack([\n                x.float().pow(2).mean(dim=(1, 2, 3)).sqrt(),\n                delta.float().pow(2).mean(dim=(1, 2, 3)).sqrt(),\n            ], dim=-1)\n            gate_logits = self.darc_controller(stats).mean(dim=0) + self.darc_depth_bias[i]\n            mix = F.softmax(gate_logits, dim=-1).to(dtype=x.dtype)\n            x = mix[0] * x + mix[1] * delta + mix[2] * x0"
7}

Tool ResultStep 8

OK: Replaced lines 168..174 with 12 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–100, 129–136, 168–179, 190–212, 271–271.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–100, 129–136, 168–179, 190–212, 271–271 | total: 461 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (7 more lines) ...
    98          x = x + self.attn(self.ln_1(x))
    99          x = x + self.mlp(self.ln_2(x))
   100          return x
...
   129          # ── DARC: Depth-Adaptive Residual Competition ──
   130          # Layer i chooses among carry x, local delta, and anchor x0 using
   131          # content-aware gates from a tiny shared controller. The controller sees
   132          # pooled statistics of x and delta, producing token-independent but
   133          # input-adaptive routing that can increase update strength only when a
   134          # layer has a confident transform to contribute.
   135

AssistantStep 9edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 190,
5  "end_line": 212,
6  "content": "    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):\n        darc_ids = {id(self.darc_controller.weight), id(self.darc_controller.bias), id(self.darc_depth_bias)}\n        param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}\n        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2 and id(p) not in darc_ids]\n        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2 and id(p) not in darc_ids]\n        darc_params = [p for n, p in param_dict.items() if id(p) in darc_ids]\n        optim_groups = [\n            {'params': decay_params, 'weight_decay': weight_decay},\n            {'params': nodecay_params, 'weight_decay': 0.0},\n            {'params': darc_params, 'weight_decay': 0.0},\n        ]\n        num_decay_params = sum(p.numel() for p in decay_params)\n        num_nodecay_params = sum(p.numel() for p in nodecay_params)\n        num_darc_params = sum(p.numel() for p in darc_params)\n        print(f\"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters\")\n        print(f\"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters\")\n        print(f\"num DARC parameter tensors: {len(darc_params)}, with {num_darc_params:,} parameters\")\n        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters\n        use_fused = fused_available and device_type == 'cuda'\n        extra_args = dict(fused=True) if use_fused else dict()\n        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)\n        print(f\"using fused AdamW: {use_fused}\")\n        return optimizer"
7}

Tool ResultStep 9

OK: Replaced lines 190..212 with 23 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–100, 129–136, 168–179, 190–212, 271–271.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–100, 129–136, 168–179, 190–212, 271–271 | total: 461 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config):
    90          super().__init__()
       ... (7 more lines) ...
    98          x = x + self.attn(self.ln_1(x))
    99          x = x + self.mlp(self.ln_2(x))
   100          return x
...
   129          # ── DARC: Depth-Adaptive Residual Competition ──
   130          # Layer i chooses among carry x, local delta, and anchor x0 using
   131          # content-aware gates from a tiny shared controller. The controller sees
   132          # pooled statistics of x and delta, producing token-independent but
   133          # input-adaptive routing that can increase update strength only when a
   134          # layer has a confident transform to contribute.
   135