Agent Conversation: gpt-5.4

Model: gpt-5.4

Total steps: 5

Messages: 12

UserStep 0

Task: llm-pretrain-mlp

LLM Pretraining: Feed-Forward Network Optimization

Research Question

Design an improved feed-forward network (MLP) for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to the standard GELU MLP.

What You Can Modify

The MLP class (lines 73-86 in custom_pretrain.py), including:

Activation function (default: GELU)
Network architecture (default: two linear layers with 4x expansion)
Gating mechanisms
Hidden dimension sizing

Constraint: The MLP must accept input of shape (B, T, n_embd) and return output of the same shape.

Evaluation

Metric: Validation loss (cross-entropy, lower is better), plus perplexity (WikiText-2, LAMBADA) and downstream accuracy (ARC-Easy, HellaSwag, PIQA, WinoGrande)
Model: GPT-2 Medium (24L/16H/1024D, ~355M params)
Dataset: FineWeb 10B (GPT-2 tokenizer), ~7.1B tokens (D=20N Chinchilla-optimal)
Training: 12030 iterations, BSZ=96, GA=6, 2-GPU DDP
Hardware: H200 GPU

nanoGPT/custom_pretrain.py [EDITABLE — lines 72–86, lines 245–247 only]

     1: """Custom GPT-2 Pretraining Script
     2: Based on Andrej Karpathy's nanoGPT, evaluated on FineWeb dataset.
     3: """
     4: 
     5: import math
     6: import inspect
     7: import os
     8: import time
     9: from contextlib import nullcontext
    10: from dataclasses import dataclass
    11: 
    12: import numpy as np
    13: import torch
    14: import torch.nn as nn
    15: from torch.nn import functional as F
    16: 
    17: # ============================================================================
    18: # Model Components
    19: # ============================================================================
    20: 
    21: # ── Normalization ──────────────────────────────────────────────────────────
    22: class LayerNorm(nn.Module):
    23:     """LayerNorm but with an optional bias."""
    24:     def __init__(self, ndim, bias):
    25:         super().__init__()
    26:         self.weight = nn.Parameter(torch.ones(ndim))
    27:         self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
    28: 
    29:     def forward(self, input):
    30:         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31: 
    32: # ── Self-Attention ─────────────────────────────────────────────────────────
    33: class CausalSelfAttention(nn.Module):
    34:     def __init__(self, config):
    35:         super().__init__()
    36:         assert config.n_embd % config.n_head == 0
    37:         self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
    38:         self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
    39:         self.attn_dropout = nn.Dropout(config.dropout)
    40:         self.resid_dropout = nn.Dropout(config.dropout)
    41:         self.n_head = config.n_head
    42:         self.n_embd = config.n_embd
    43:         self.dropout

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 72,
5  "end_line": 86,
6  "content": "class MLP(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        # Novel contribution: variance-controlled dual-gated MLP.\n        # We keep parameter count close to the standard 4x MLP by using\n        # two 2x branches: one smooth positive gate and one signed feature branch.\n        hidden_dim = 2 * config.n_embd\n        self.wg = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n        self.wu = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n        self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)\n        self.dropout = nn.Dropout(config.dropout)\n        # Fixed blend balances stable low-curvature GELU gating with stronger\n        # SwiGLU-style multiplicative interactions.\n        self.gate_blend = 0.5\n\n    def forward(self, x):\n        g = self.wg(x)\n        u = self.wu(x)\n        # Blend two complementary gates on the same preactivation.\n        # sigmoid gate controls variance, while GELU gate preserves rich curvature.\n        gate = self.gate_blend * torch.sigmoid(g) + (1.0 - self.gate_blend) * F.gelu(g)\n        x = u * gate\n        x = self.c_proj(x)\n        x = self.dropout(x)\n        return x\n"
7}

Tool ResultStep 1

OK: Replaced lines 72..86 with 25 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–96, 255–257.

[Current file: nanoGPT/custom_pretrain.py | editable: 72–96, 255–257 | total: 447 lines]
    72  class MLP(nn.Module):
    73      def __init__(self, config):
    74          super().__init__()
       ... (19 more lines) ...
    94          x = self.c_proj(x)
    95          x = self.dropout(x)
    96          return x
...
   255      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   256      # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
   257      CONFIG_OVERRIDES = {}

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=8000, train_loss=2.6669, val_loss=2.3922
TRAIN_METRICS: step=9000, train_loss=2.6306, val_loss=2.3515
TRAIN_METRICS: step=10000, train_loss=2.6292, val_loss=2.3397
TRAIN_METRICS: step=11000, train_loss=2.6000, val_loss=2.3311
TRAIN_METRICS: step=12000, train_loss=2.6007, val_loss=2.3214
Final metrics (gpt-345m): val_loss_gpt-345m=2.3212, wikitext2_ppl_gpt-345m=45.3300, lambada_ppl_gpt-345m=70.5500

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.6700, hellaswag_lm-eval-345m=32.8400, piqa_lm-eval-345m=63.8700, winogrande_lm-eval-345m=52.3300

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3212, 'wikitext2_ppl_gpt-345m': 45.33, 'lambada_ppl_gpt-345m': 70.55, 'elapsed_gpt-345m': 18693, 'arc_easy_lm-eval-345m': 54.67, 'hellaswag_lm-eval-345m': 32.84, 'piqa

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 72,
5  "end_line": 96,
6  "content": "class MLP(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        # Novel contribution: normalized squared-SiLU GLU (NSS-GLU).\n        # We use a 2x hidden branch for features and a 2x hidden branch for gates,\n        # preserving the standard 4x parameter budget while introducing a sharper,\n        # always-positive gate. A fixed normalization keeps activation scale close\n        # to standard GLU variants for stable optimization.\n        hidden_dim = 2 * config.n_embd\n        self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n        self.w2 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n        self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)\n        self.dropout = nn.Dropout(config.dropout)\n        self.gate_norm = 1.27\n\n    def forward(self, x):\n        u = self.w1(x)\n        g = self.w2(x)\n        # Squared SiLU gate increases selectivity over SwiGLU while remaining smooth.\n        gate = F.silu(g)\n        x = u * (gate * gate / self.gate_norm)\n        x = self.c_proj(x)\n        x = self.dropout(x)\n        return x\n"
7}

Tool ResultStep 3

OK: Replaced lines 72..96 with 24 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–95, 254–256.

[Current file: nanoGPT/custom_pretrain.py | editable: 72–95, 254–256 | total: 446 lines]
    72  class MLP(nn.Module):
    73      def __init__(self, config):
    74          super().__init__()
       ... (18 more lines) ...
    93          x = self.c_proj(x)
    94          x = self.dropout(x)
    95          return x
...
   254      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   255      # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
   256      CONFIG_OVERRIDES = {}

AssistantStep 4test

Tool ResultStep 4

[Test #2] (1 test 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=8000, train_loss=2.6804, val_loss=2.4081
TRAIN_METRICS: step=9000, train_loss=2.6445, val_loss=2.3675
TRAIN_METRICS: step=10000, train_loss=2.6436, val_loss=2.3557
TRAIN_METRICS: step=11000, train_loss=2.6149, val_loss=2.3478
TRAIN_METRICS: step=12000, train_loss=2.6159, val_loss=2.3379
Final metrics (gpt-345m): val_loss_gpt-345m=2.3380, wikitext2_ppl_gpt-345m=45.7600, lambada_ppl_gpt-345m=70.6600

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.1200, hellaswag_lm-eval-345m=32.6900, piqa_lm-eval-345m=63.6600, winogrande_lm-eval-345m=52.8800

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.338, 'wikitext2_ppl_gpt-345m': 45.76, 'lambada_ppl_gpt-345m': 70.66, 'elapsed_gpt-345m': 17963, 'arc_easy_lm-eval-345m': 54.12, 'hellaswag_lm-eval-345m': 32.69, 'piqa_l

AssistantStep 5submit

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 5

[submit] Submitting result from test #1 as final.

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6669, val_loss=2.3922
TRAIN_METRICS: step=9000, train_loss=2.6306, val_loss=2.3515
TRAIN_METRICS: step=10000, train_loss=2.6292, val_loss=2.3397
TRAIN_METRICS: step=11000, train_loss=2.6000, val_loss=2.3311
TRAIN_METRICS: step=12000, train_loss=2.6007, val_loss=2.3214
Final metrics (gpt-345m): val_loss_gpt-345m=2.3212, wikitext2_ppl_gpt-345m=45.3300, lambada_ppl_gpt-345m=70.5500

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.6700, hellaswag_lm-eval-345m=32.8400, piqa_lm-eval-345m=63.8700, winogrande_lm-eval-345m=52.3300

[submit] Finalized test #1 as final.

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3212, 'wikitext2_ppl_gpt-345m': 45.33, 'lambada_ppl_gpt-345m': 70.55, 'elapsed_gpt-345m': 18693, 'arc_easy_lm-eval-345m': 54.67, 'hellaswag_lm-eval-345m': 32.84, 'piqa_lm-eva