Agent Conversation: qwen3.6-plus

Model: qwen3.6-plus
Total steps: 18
Messages: 40
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:

  1. Per-layer residual scaling: Learnable scalars that modulate the residual stream at each layer (inspired by modded-nanogpt, ReZero, SkipInit).
  2. Initial embedding blending: Blending the initial token embedding back at each layer to preserve token identity (x0 residual connections).
  3. Hyper-Connections: Maintaining m parallel residual streams with learned transition matrices for richer information flow across layers (Zhu et al., 2025).
  4. 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  "content": "        # ── ADGRes: Adaptive Dual-Gating Residual ──\n        # Per-layer gating: alpha scales attention residual, beta scales MLP residual\n        # x0_lambda blends initial embedding back at each layer\n        # Warmup: gate values start near 0 and ramp up to learned values\n        self.attn_alphas = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.mlp_betas = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))\n        # Warmup hyperparameters (hardcoded)\n        self.adgres_warmup_T = 2000  # steps for gates to reach learned values",
5  "start_line": 128,
6  "end_line": 130
7}
Tool ResultStep 1
OK: Replaced lines 128..130 with 9 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–99, 128–136, 168–170, 181–198, 257–257.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–99, 128–136, 168–170, 181–198, 257–257 | total: 447 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
...
   128          # ── ADGRes: Adaptive Dual-Gating Residual ──
   129          # Per-layer gating: alpha scales attention residual, beta scales MLP residual
   130          # x0_lambda blends initial embedding back at each layer
       ... (3 more lines) ...
   134          self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))
   135          # Warmup hyperparameters (hardcoded)
   136          self.adgres_warmup_T = 2000  # steps for gates to reach learned values
...
   168
AssistantStep 2edit
Tool Input
1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "content": "class Block(nn.Module):\n    def __init__(self, config, layer_idx=0):\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, alpha=1.0, beta=1.0, x0=None, x0_lambda=0.0):\n        h_attn = self.attn(self.ln_1(x))\n        x = x + alpha * h_attn\n        h_mlp = self.mlp(self.ln_2(x))\n        x = x + beta * h_mlp\n        if x0 is not None and x0_lambda != 0.0:\n            x = x + x0_lambda * x0\n        return x",
5  "start_line": 88,
6  "end_line": 99
7}
Tool ResultStep 2
OK: Replaced lines 88..99 with 16 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–103, 132–140, 172–174, 185–202, 261–261.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–103, 132–140, 172–174, 185–202, 261–261 | total: 451 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config, layer_idx=0):
    90          super().__init__()
       ... (10 more lines) ...
   101          if x0 is not None and x0_lambda != 0.0:
   102              x = x + x0_lambda * x0
   103          return x
...
   132          # ── ADGRes: Adaptive Dual-Gating Residual ──
   133          # Per-layer gating: alpha scales attention residual, beta scales MLP residual
   134          # x0_lambda blends initial embedding back at each layer
       ... (3 more lines) ...
   138          self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))
   139          # Warmup hyperparameters (hardcoded)
   140          self.adgres_warmup_T = 2000  # steps for gates to reach learned val
AssistantStep 3edit
Tool Input
1{
2  "content": "        # ── ADGRes: iterate through transformer blocks with gating ──\n        if self.training:\n            self._prores_step += 1\n        step = getattr(self, '_prores_step', torch.tensor(0, device=device)).item()\n        T = self.adgres_warmup_T\n        x0 = x\n        for i, block in enumerate(self.transformer.h):\n            # Warmup gating: start near 0, ramp to learned values\n            if self.training and step < T:\n                warmup_factor = step / T\n                alpha = warmup_factor * self.attn_alphas[i].item()\n                beta = warmup_factor * self.mlp_betas[i].item()\n                x0_l = warmup_factor * self.x0_lambdas[i].item()\n            else:\n                alpha = self.attn_alphas[i].item()\n                beta = self.mlp_betas[i].item()\n                x0_l = self.x0_lambdas[i].item()\n            x = block(x, alpha=alpha, beta=beta, x0=x0, x0_lambda=x0_l)",
3  "start_line": 172,
4  "end_line": 174,
5  "filename": "nanoGPT/custom_pretrain.py"
6}
Tool ResultStep 3
ERROR: Invalid arguments for 'edit': WorkspaceTools.edit() missing 1 required positional argument: 'op'
AssistantStep 4edit
Tool Input
1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "content": "        # ── ADGRes: iterate through transformer blocks with gating ──\n        if self.training:\n            self._prores_step += 1\n        step = getattr(self, '_prores_step', torch.tensor(0, device=device)).item()\n        T = self.adgres_warmup_T\n        x0 = x\n        for i, block in enumerate(self.transformer.h):\n            # Warmup gating: start near 0, ramp to learned values\n            if self.training and step < T:\n                warmup_factor = step / T\n                alpha = warmup_factor * self.attn_alphas[i].item()\n                beta = warmup_factor * self.mlp_betas[i].item()\n                x0_l = warmup_factor * self.x0_lambdas[i].item()\n            else:\n                alpha = self.attn_alphas[i].item()\n                beta = self.mlp_betas[i].item()\n                x0_l = self.x0_lambdas[i].item()\n            x = block(x, alpha=alpha, beta=beta, x0=x0, x0_lambda=x0_l)",
5  "start_line": 172,
6  "end_line": 174
7}
Tool ResultStep 4
OK: Replaced lines 172..174 with 18 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–103, 132–140, 172–189, 200–217, 276–276.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–103, 132–140, 172–189, 200–217, 276–276 | total: 466 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config, layer_idx=0):
    90          super().__init__()
       ... (10 more lines) ...
   101          if x0 is not None and x0_lambda != 0.0:
   102              x = x + x0_lambda * x0
   103          return x
...
   132          # ── ADGRes: Adaptive Dual-Gating Residual ──
   133          # Per-layer gating: alpha scales attention residual, beta scales MLP residual
   134          # x0_lambda blends initial embedding back at each layer
       ... (3 more lines) ...
   138          self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))
   139          # Warmup hyperparameters (hardcoded)
   140          self.adgres_warmup_T = 2000  # steps for gates to reach learned v
AssistantStep 5edit
Tool Input
1{
2  "content": "        # ── ADGRes: Adaptive Dual-Gating Residual ──\n        # Per-layer gating: alpha scales attention residual, beta scales MLP residual\n        # x0_lambda blends initial embedding back at each layer\n        # Warmup: gate values start near 0 and ramp up to learned values\n        self.attn_alphas = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.mlp_betas = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))\n        # Warmup hyperparameters (hardcoded)\n        self.adgres_warmup_T = 2000  # steps for gates to reach learned values\n        # Step counter for warmup\n        self.register_buffer('_prores_step', torch.zeros(1, dtype=torch.long))",
3  "start_line": 132,
4  "end_line": 140,
5  "filename": "nanoGPT/custom_pretrain.py",
6  "op": "replace"
7}
Tool ResultStep 5
OK: Replaced lines 132..140 with 11 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–103, 132–142, 174–191, 202–219, 278–278.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–103, 132–142, 174–191, 202–219, 278–278 | total: 468 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config, layer_idx=0):
    90          super().__init__()
       ... (10 more lines) ...
   101          if x0 is not None and x0_lambda != 0.0:
   102              x = x + x0_lambda * x0
   103          return x
...
   132          # ── ADGRes: Adaptive Dual-Gating Residual ──
   133          # Per-layer gating: alpha scales attention residual, beta scales MLP residual
   134          # x0_lambda blends initial embedding back at each layer
       ... (5 more lines) ...
   140          self.adgres_warmup_T = 2000  # steps for gates to reach learned values
   141          # Step counter for warmup
   142          self.register_buffer('_prores_step', torch.zeros(1, dtype=torch.l
AssistantStep 6edit
Tool Input
1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "content": "    # ── Optimizer Configuration ────────────────────────────────────────────\n    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):\n        # Separate gating params from main model params\n        gating_ids = {id(self.attn_alphas), id(self.mlp_betas), id(self.x0_lambdas)}\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 gating_ids]\n        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2 and id(p) not in gating_ids]\n        gating_params = [p for n, p in param_dict.items() if id(p) in gating_ids]\n        optim_groups = [\n            {'params': decay_params, 'weight_decay': weight_decay},\n            {'params': nodecay_params, 'weight_decay': 0.0},\n            {'params': gating_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_gating_params = sum(p.numel() for p in gating_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 gating parameter tensors: {len(gating_params)}, with {num_gating_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",
5  "start_line": 202,
6  "end_line": 219
7}
Tool ResultStep 6
OK: Replaced lines 202..219 with 25 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–103, 132–142, 174–191, 202–226, 285–285.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–103, 132–142, 174–191, 202–226, 285–285 | total: 475 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config, layer_idx=0):
    90          super().__init__()
       ... (10 more lines) ...
   101          if x0 is not None and x0_lambda != 0.0:
   102              x = x + x0_lambda * x0
   103          return x
...
   132          # ── ADGRes: Adaptive Dual-Gating Residual ──
   133          # Per-layer gating: alpha scales attention residual, beta scales MLP residual
   134          # x0_lambda blends initial embedding back at each layer
       ... (5 more lines) ...
   140          self.adgres_warmup_T = 2000  # steps for gates to reach learned values
   141          # Step counter for warmup
   142          self.register_buffer('_prores_step', torch.zeros(1, dtype=torch.l
AssistantStep 7test
Tool ResultStep 7
[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### gpt-345m (scripts/gpt_345m.sh)
[COMMAND FAILED exit=1]
W0413 00:52:28.428000 731664 site-packages/torch/distributed/run.py:792] 
W0413 00:52:28.428000 731664 site-packages/torch/distributed/run.py:792] *****************************************
W0413 00:52:28.428000 731664 site-packages/torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0413 00:52:28.428000 731664 site-packages/torch/distributed/run.py:792] *****************************************
[W413 00:52:28.478130404 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3
[W413 00:52:28.478401285 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3
[W413 00:52:28.646128582 socket.cpp:20
AssistantStep 8edit
Tool Input
1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "content": "        # ── ADGRes: Adaptive Dual-Gating Residual ──\n        # Per-layer gating: alpha scales attention residual, beta scales MLP residual\n        # x0_lambda blends initial embedding back at each layer\n        self.attn_alphas = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.mlp_betas = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))",
5  "start_line": 132,
6  "end_line": 142
7}
Tool ResultStep 8
OK: Replaced lines 132..142 with 6 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–103, 132–137, 169–186, 197–221, 280–280.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–103, 132–137, 169–186, 197–221, 280–280 | total: 470 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config, layer_idx=0):
    90          super().__init__()
       ... (10 more lines) ...
   101          if x0 is not None and x0_lambda != 0.0:
   102              x = x + x0_lambda * x0
   103          return x
...
   132          # ── ADGRes: Adaptive Dual-Gating Residual ──
   133          # Per-layer gating: alpha scales attention residual, beta scales MLP residual
   134          # x0_lambda blends initial embedding back at each layer
   135          self.attn_alphas = nn.Parameter(torch.ones(config.n_layer) * 0.9)
   136          self.mlp_betas = nn.Parameter(torch.ones(config.n_layer) * 0.9)
   137          self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))
...
AssistantStep 9edit
Tool Input
1{
2  "content": "        # ── ADGRes: Adaptive Dual-Gating Residual ──\n        # Per-layer gating: alpha scales attention residual, beta scales MLP residual\n        # x0_lambda blends initial embedding back at each layer\n        self.attn_alphas = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.mlp_betas = nn.Parameter(torch.ones(config.n_layer) * 0.9)\n        self.x0_lambdas = nn.Parameter(torch.zeros(config.n_layer))\n        # Warmup hyperparameters (hardcoded)\n        self.adgres_warmup_T = 2000  # steps for gates to reach learned values\n        # Step counter for warmup\n        self.register_buffer('_prores_step', torch.zeros(1, dtype=torch.long))",
3  "start_line": 132,
4  "end_line": 137,
5  "filename": "nanoGPT/custom_pretrain.py",
6  "op": "replace"
7}
Tool ResultStep 9
OK: Replaced lines 132..137 with 10 line(s) in nanoGPT/custom_pretrain.py. Editable range: 88–103, 132–141, 173–190, 201–225, 284–284.

[Current file: nanoGPT/custom_pretrain.py | editable: 88–103, 132–141, 173–190, 201–225, 284–284 | total: 474 lines]
    88  class Block(nn.Module):
    89      def __init__(self, config, layer_idx=0):
    90          super().__init__()
       ... (10 more lines) ...
   101          if x0 is not None and x0_lambda != 0.0:
   102              x = x + x0_lambda * x0
   103          return x
...
   132          # ── ADGRes: Adaptive Dual-Gating Residual ──
   133          # Per-layer gating: alpha scales attention residual, beta scales MLP residual
   134          # x0_lambda blends initial embedding back at each layer
       ... (4 more lines) ...
   139          self.adgres_warmup_T = 2000  # steps for gates to reach learned values
   140          # Step counter for warmup
   141          self.register_buffer('_prores_step', torch.zeros(1, dtype=torch.l