Agent Conversation: gemini-3.1-pro-preview
Model: gemini-3.1-pro-preview
Total steps: 8
Messages: 20
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)
- Model sizes: GPT-2 124M (12L/12H/768D) and GPT-2 1.5B (48L/25H/1600D, 4-GPU DDP)
- Dataset: FineWeb 10B (GPT-2 tokenizer)
- Training: 5000 iterations, batch_size=12, block_size=1024, grad_accum=5
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 = config.dropout
44: self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
45: AssistantStep 1edit
Tool Input
1{
2 "start_line": 72,
3 "content": "class MLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n # Expanding dimension matched to 8/3 to retain parameter parity with standard expanded MLP\n hidden_dim = int(8 / 3 * config.n_embd)\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.w3 = nn.Linear(config.n_embd, bias=config.bias)\n \n # Post-gating normalization stabilizes the unbounded squared activations\n self.norm = LayerNorm(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\n def forward(self, x):\n # Normed Squared-ReLU Gating Function:\n # F.relu().square() accelerates training by preventing gradient saturation,\n # while self.norm tightly conditions the resulting landscape to avoid instability.\n h = F.relu(self.w1(x)).square() * self.w3(x)\n h = self.norm(h)\n return self.dropout(self.c_proj(h))",
4 "filename": "custom_pretrain.py",
5 "end_line": 86,
6 "op": "replace"
7}Tool ResultStep 1
ERROR: Package 'custom_pretrain.py' is not in allowed packages
AssistantStep 2edit
Tool Input
1{
2 "filename": "nanoGPT/custom_pretrain.py",
3 "op": "replace",
4 "start_line": 72,
5 "content": "class MLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n # Expanding dimension matched to 8/3 to retain parameter parity with standard expanded MLP\n hidden_dim = int(8 / 3 * config.n_embd)\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.w3 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n \n # Post-gating normalization stabilizes the unbounded squared activations\n self.norm = LayerNorm(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\n def forward(self, x):\n # Normed Squared-ReLU Gating Function:\n # F.relu().square() accelerates training by preventing gradient saturation,\n # while self.norm tightly conditions the resulting landscape to avoid instability.\n h = F.relu(self.w1(x)).square() * self.w3(x)\n h = self.norm(h)\n return self.dropout(self.c_proj(h))",
6 "end_line": 86
7}Tool ResultStep 2
OK: Replaced lines 72..86 with 21 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–92, 251–253.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–92 | total: 443 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 class MLP(nn.Module):
73 def __init__(self, config):
74 super().__init__()
75 # Expanding dimension matched to 8/3 to retain parameter parity with standard expanded MLP
76 hidden_dim = int(8 / 3 * config.n_embd)
77 hidden_dim = ((hidden_dim + 63) // 64) * 64
78 self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
79 self.w3 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
80
81 # Post-gating normalization stabilizes the unbounded squared activations
82 self.norm = LayerNorm(hidden_dim, bias=config.bias)
83 self.c_proj = n
AssistantStep 3test
Tool ResultStep 3
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6316, val_loss=2.3552
TRAIN_METRICS: step=9000, train_loss=2.6037, val_loss=2.3354
TRAIN_METRICS: step=10000, train_loss=2.5893, val_loss=2.3146
TRAIN_METRICS: step=11000, train_loss=2.5830, val_loss=2.3056
TRAIN_METRICS: step=12000, train_loss=2.5660, val_loss=2.2908
Final metrics (gpt-345m): val_loss_gpt-345m=2.2923, wikitext2_ppl_gpt-345m=43.3500, lambada_ppl_gpt-345m=66.2300
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.4200, hellaswag_lm-eval-345m=33.3400, piqa_lm-eval-345m=64.8000, winogrande_lm-eval-345m=51.1400
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2923, 'wikitext2_ppl_gpt-345m': 43.35, 'lambada_ppl_gpt-345m': 66.23, 'elapsed_gpt-345m': 22175, 'arc_easy_lm-eval-345m': 54.42, 'hellaswag_lm-eval-345m': 33.34, 'piqa_lm-eval-345m': 64.8, 'wi
AssistantStep 4edit
Tool Input
1{
2 "start_line": 72,
3 "op": "replace",
4 "filename": "nanoGPT/custom_pretrain.py",
5 "content": "class MLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n # Target ~ 4.7M params via 8/3 expansion for SwiGLU\n hidden_dim = int(8 / 3 * config.n_embd)\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n \n self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.w3 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n \n # Norm layer inside MLP to stabilize gating outputs\n self.norm = LayerNorm(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\n def forward(self, x):\n # Normed SwiGLU balances the rich gating interactions of Swish \n # with stable projection dynamics\n h = F.silu(self.w1(x)) * self.w3(x)\n h = self.norm(h)\n return self.dropout(self.c_proj(h))",
6 "end_line": 92
7}Tool ResultStep 4
OK: Replaced lines 72..92 with 21 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–92, 251–253.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–92 | total: 443 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 class MLP(nn.Module):
73 def __init__(self, config):
74 super().__init__()
75 # Target ~ 4.7M params via 8/3 expansion for SwiGLU
76 hidden_dim = int(8 / 3 * config.n_embd)
77 hidden_dim = ((hidden_dim + 63) // 64) * 64
78
79 self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
80 self.w3 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
81
82 # Norm layer inside MLP to stabilize gating outputs
83 self.norm = LayerNorm(hidden_dim, bias=config.bias)
84 self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=co
AssistantStep 5test
AssistantStep 5test
Tool ResultStep 5
[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=2.6401, val_loss=2.3638
TRAIN_METRICS: step=9000, train_loss=2.6117, val_loss=2.3440
TRAIN_METRICS: step=10000, train_loss=2.5970, val_loss=2.3231
TRAIN_METRICS: step=11000, train_loss=2.5898, val_loss=2.3142
TRAIN_METRICS: step=12000, train_loss=2.5725, val_loss=2.2989
Final metrics (gpt-345m): val_loss_gpt-345m=2.2999, wikitext2_ppl_gpt-345m=44.4200, lambada_ppl_gpt-345m=68.1300
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.5500, hellaswag_lm-eval-345m=33.3300, piqa_lm-eval-345m=64.2500, winogrande_lm-eval-345m=51.3000
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2999, 'wikitext2_ppl_gpt-345m': 44.42, 'lambada_ppl_gpt-345m': 68.13, 'elapsed_gpt-345m': 22322, 'arc_easy_lm-eval-345m': 54.55, 'hellaswag_lm-eval-345m': 33.33, 'piqa_
AssistantStep 6edit
Tool Input
1{
2 "content": "class MLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n # Use a higher expansion ratio (since we don't have gating w3 layer, we can afford it)\n # But wait, capacity is fixed. wait... expanding dimension matched to 8/3 for gating is param-equal to 4x expansion for standard.\n # So we keep 4x expansion.\n hidden_dim = 4 * config.n_embd\n \n self.c_fc = 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 \n # Trainable scaling factor for the activation function\n self.alpha = nn.Parameter(torch.ones(1))\n # Trainable shifting factor for the activation function\n self.beta = nn.Parameter(torch.zeros(1))\n\n def forward(self, x):\n h = self.c_fc(x)\n # Shifted softplus gating:\n h = h * F.sigmoid(self.alpha * h + self.beta)\n return self.dropout(self.c_proj(h))",
3 "end_line": 92,
4 "start_line": 72,
5 "op": "replace",
6 "filename": "nanoGPT/custom_pretrain.py"
7}Tool ResultStep 6
OK: Replaced lines 72..92 with 22 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–93.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–93 | total: 444 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 class MLP(nn.Module):
73 def __init__(self, config):
74 super().__init__()
75 # Use a higher expansion ratio (since we don't have gating w3 layer, we can afford it)
76 # But wait, capacity is fixed. wait... expanding dimension matched to 8/3 for gating is param-equal to 4x expansion for standard.
77 # So we keep 4x expansion.
78 hidden_dim = 4 * config.n_embd
79
80 self.c_fc = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
81 self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)
82 self.dropout = nn.Dropout(config.dropout)
83
AssistantStep 7test
AssistantStep 7test
Tool ResultStep 7
[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)
[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6483, val_loss=2.3694
TRAIN_METRICS: step=9000, train_loss=2.6116, val_loss=2.3370
TRAIN_METRICS: step=10000, train_loss=2.6002, val_loss=2.3235
TRAIN_METRICS: step=11000, train_loss=2.5769, val_loss=2.2961
TRAIN_METRICS: step=12000, train_loss=2.5733, val_loss=2.2933
Final metrics (gpt-345m): val_loss_gpt-345m=2.2872, wikitext2_ppl_gpt-345m=44.8700, lambada_ppl_gpt-345m=69.3300
### lm-eval-345m (scripts/lm_eval_345m.sh)
ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-mlp/gemini-3.1-pro-preview_20260408_123006/seed_42/ckpt_gpt-345m.pt
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2872, 'wikitext2_ppl_gpt-345m': 44.87, 'lam
AssistantStep 8submit
Tool Input
1{
2 "n": 1
3}Tool ResultStep 8
[submit] Submitting result from test #1 as final.
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6316, val_loss=2.3552
TRAIN_METRICS: step=9000, train_loss=2.6037, val_loss=2.3354
TRAIN_METRICS: step=10000, train_loss=2.5893, val_loss=2.3146
TRAIN_METRICS: step=11000, train_loss=2.5830, val_loss=2.3056
TRAIN_METRICS: step=12000, train_loss=2.5660, val_loss=2.2908
Final metrics (gpt-345m): val_loss_gpt-345m=2.2923, wikitext2_ppl_gpt-345m=43.3500, lambada_ppl_gpt-345m=66.2300
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.4200, hellaswag_lm-eval-345m=33.3400, piqa_lm-eval-345m=64.8000, winogrande_lm-eval-345m=51.1400
[submit] Finalized test #1 as final.
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2923, 'wikitext2_ppl_gpt-345m': 43.35, 'lambada_ppl_gpt-345m': 66.23, 'elapsed_gpt-345m': 22175, 'arc_easy_lm-eval-345m': 54.42, 'hellaswag_lm-eval-345m': 33.34, 'piqa_lm-eva