Agent Conversation: gemini-3.1-pro-preview

Model: gemini-3.1-pro-preview

Total steps: 8

Messages: 19

UserStep 0

Task: llm-pretrain-lr-schedule

LLM Pretraining: Learning Rate Schedule Optimization

Research Question

Design an improved learning rate schedule for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to the standard cosine annealing schedule with linear warmup.

What You Can Modify

The get_lr function (lines 192-201) in custom_pretrain.py:

Schedule shape (default: cosine decay with linear warmup)
Warmup strategy and duration
Decay behavior (shape, rate, final LR)
Multi-phase scheduling (e.g., warmup-stable-decay)

Note: The function signature get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr) must be preserved. The training loop calls this function at every iteration to set the learning rate.

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 191–201, 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

AssistantStep 1edit

Tool Input

1{
2  "filename": "custom_pretrain.py",
3  "end_line": 201,
4  "start_line": 191,
5  "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n    \"\"\"Warmup, Slow Linear Decay, Fast Cosine Cooldown (WSFC).\"\"\"\n    if it < warmup_iters:\n        return learning_rate * (it + 1) / (warmup_iters + 1)\n    \n    cooldown_start = int(lr_decay_iters * 0.8)\n    mid_lr = learning_rate * 0.5\n    \n    if it < cooldown_start:\n        ratio = (it - warmup_iters) / (cooldown_start - warmup_iters)\n        return learning_rate - ratio * (learning_rate - mid_lr)\n        \n    if it > lr_decay_iters:\n        return min_lr\n        \n    ratio = (it - cooldown_start) / (lr_decay_iters - cooldown_start)\n    coeff = 0.5 * (1.0 + math.cos(math.pi * ratio))\n    return min_lr + coeff * (mid_lr - min_lr)",
6  "op": "replace"
7}

Tool ResultStep 1

ERROR: Package 'custom_pretrain.py' is not in allowed packages

AssistantStep 2edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "end_line": 201,
5  "start_line": 191,
6  "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n    \"\"\"Warmup, Slow Linear Decay, Fast Cosine Cooldown (WSFC).\"\"\"\n    if it < warmup_iters:\n        return learning_rate * (it + 1) / (warmup_iters + 1)\n    \n    cooldown_start = int(lr_decay_iters * 0.8)\n    mid_lr = learning_rate * 0.5\n    \n    if it < cooldown_start:\n        ratio = (it - warmup_iters) / (cooldown_start - warmup_iters)\n        return learning_rate - ratio * (learning_rate - mid_lr)\n        \n    if it > lr_decay_iters:\n        return min_lr\n        \n    ratio = (it - cooldown_start) / (lr_decay_iters - cooldown_start)\n    coeff = 0.5 * (1.0 + math.cos(math.pi * ratio))\n    return min_lr + coeff * (mid_lr - min_lr)"
7}

Tool ResultStep 2

OK: Replaced lines 191..201 with 18 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–208, 252–254.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–208 | total: 444 lines]
   188|         return optimizer
   189| 
   190| # ── Learning Rate Schedule ─────────────────────────────────────────────────
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Warmup, Slow Linear Decay, Fast Cosine Cooldown (WSFC)."""
   193      if it < warmup_iters:
   194          return learning_rate * (it + 1) / (warmup_iters + 1)
   195      
   196      cooldown_start = int(lr_decay_iters * 0.8)
   197      mid_lr = learning_rate * 0.5
   198      
   199      if it < cooldown_start:
   200          ratio = (it - warmup_iters) / (cooldown_start - warmup_iters)
   201          return learning_rate - ratio * (learning_rate - mid_lr)
   202          
   203      if it > lr_decay_iters:
   204          return min_lr
   205          
   206      ratio

AssistantStep 3test

Tool ResultStep 3

[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=2.6757, val_loss=2.3962
TRAIN_METRICS: step=9000, train_loss=2.6404, val_loss=2.3660
TRAIN_METRICS: step=10000, train_loss=2.6285, val_loss=2.3523
TRAIN_METRICS: step=11000, train_loss=2.5819, val_loss=2.3016
TRAIN_METRICS: step=12000, train_loss=2.5589, val_loss=2.2826
Final metrics (gpt-345m): val_loss_gpt-345m=2.2750, wikitext2_ppl_gpt-345m=43.8800, lambada_ppl_gpt-345m=67.4300

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.4800, hellaswag_lm-eval-345m=33.6300, piqa_lm-eval-345m=63.2200, winogrande_lm-eval-345m=50.6700

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.275, 'wikitext2_ppl_gpt-345m': 43.88, 'lambada_ppl_gpt-345m': 67.43, 'elapsed_gpt-345m': 20562, 'arc_easy_lm-eval-345m': 56.48, 'hellaswag_lm-eval-345m': 33.63, 'piqa_

AssistantStep 4edit

Tool Input

1{
2  "start_line": 191,
3  "filename": "nanoGPT/custom_pretrain.py",
4  "op": "replace",
5  "end_line": 208,
6  "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n    \"\"\"Two-Stage Cosine Decay (TSCD): Slow cosine decay then fast cosine cooldown.\"\"\"\n    if it < warmup_iters:\n        return learning_rate * (it + 1) / (warmup_iters + 1)\n        \n    cooldown_start = int(lr_decay_iters * 0.8)\n    mid_lr = learning_rate * 0.7\n    \n    if it < cooldown_start:\n        ratio = (it - warmup_iters) / (cooldown_start - warmup_iters)\n        coeff = 0.5 * (1.0 + math.cos(math.pi * ratio))\n        return mid_lr + coeff * (learning_rate - mid_lr)\n        \n    if it > lr_decay_iters:\n        return min_lr\n        \n    ratio = (it - cooldown_start) / (lr_decay_iters - cooldown_start)\n    coeff = 0.5 * (1.0 + math.cos(math.pi * ratio))\n    return min_lr + coeff * (mid_lr - min_lr)"
7}

Tool ResultStep 4

OK: Replaced lines 191..208 with 19 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–209, 253–255.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–209 | total: 445 lines]
   188|         return optimizer
   189| 
   190| # ── Learning Rate Schedule ─────────────────────────────────────────────────
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Two-Stage Cosine Decay (TSCD): Slow cosine decay then fast cosine cooldown."""
   193      if it < warmup_iters:
   194          return learning_rate * (it + 1) / (warmup_iters + 1)
   195          
   196      cooldown_start = int(lr_decay_iters * 0.8)
   197      mid_lr = learning_rate * 0.7
   198      
   199      if it < cooldown_start:
   200          ratio = (it - warmup_iters) / (cooldown_start - warmup_iters)
   201          coeff = 0.5 * (1.0 + math.cos(math.pi * ratio))
   202          return mid_lr + coeff * (learning_rate - mid_lr)
   203          
   204      if it >

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=2.6771, val_loss=2.3969
TRAIN_METRICS: step=9000, train_loss=2.6470, val_loss=2.3702
TRAIN_METRICS: step=10000, train_loss=2.6311, val_loss=2.3530
TRAIN_METRICS: step=11000, train_loss=2.5653, val_loss=2.2849
TRAIN_METRICS: step=12000, train_loss=2.5278, val_loss=2.2510
Final metrics (gpt-345m): val_loss_gpt-345m=2.2433, wikitext2_ppl_gpt-345m=41.6000, lambada_ppl_gpt-345m=64.2200

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.9900, hellaswag_lm-eval-345m=34.1700, piqa_lm-eval-345m=63.6600, winogrande_lm-eval-345m=50.5900

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2433, 'wikitext2_ppl_gpt-345m': 41.6, 'lambada_ppl_gpt-345m': 64.22, 'elapsed_gpt-345m': 20353, 'arc_easy_lm-eval-345m': 56.99, 'hellaswag_lm-eval-345m': 34.17, 'piqa_l

AssistantStep 6edit

Tool Input

1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n    \"\"\"Two-Stage Cosine Decay (TSCD): Slow cosine decay then fast cosine cooldown.\"\"\"\n    if it < warmup_iters:\n        return learning_rate * (it + 1) / (warmup_iters + 1)\n        \n    cooldown_start = int(lr_decay_iters * 0.8)\n    mid_lr = learning_rate * 0.8\n    \n    if it < cooldown_start:\n        ratio = (it - warmup_iters) / (cooldown_start - warmup_iters)\n        coeff = 0.5 * (1.0 + math.cos(math.pi * ratio))\n        return mid_lr + coeff * (learning_rate - mid_lr)\n        \n    if it > lr_decay_iters:\n        return min_lr\n        \n    ratio = (it - cooldown_start) / (lr_decay_iters - cooldown_start)\n    coeff = 0.5 * (1.0 + math.cos(math.pi * ratio))\n    return min_lr + coeff * (mid_lr - min_lr)",
4  "start_line": 191,
5  "end_line": 209,
6  "op": "replace"
7}

Tool ResultStep 6

OK: Replaced lines 191..209 with 19 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–209.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–209 | total: 447 lines]
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Two-Stage Cosine Decay (TSCD): Slow cosine decay then fast cosine cooldown."""
   193      if it < warmup_iters:
       ... (13 more lines) ...
   207      ratio = (it - cooldown_start) / (lr_decay_iters - cooldown_start)
   208      coeff = 0.5 * (1.0 + math.cos(math.pi * ratio))
   209      return min_lr + coeff * (mid_lr - min_lr)

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=2.6735, val_loss=2.3954
TRAIN_METRICS: step=9000, train_loss=2.6414, val_loss=2.3675
TRAIN_METRICS: step=10000, train_loss=2.6300, val_loss=2.3534
TRAIN_METRICS: step=11000, train_loss=2.5706, val_loss=2.2928
TRAIN_METRICS: step=12000, train_loss=2.5378, val_loss=2.2626
Final metrics (gpt-345m): val_loss_gpt-345m=2.2551, wikitext2_ppl_gpt-345m=42.2200, lambada_ppl_gpt-345m=64.7800

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.8200, hellaswag_lm-eval-345m=33.7600, piqa_lm-eval-345m=64.0900, winogrande_lm-eval-345m=49.7200

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2551, 'wikitext2_ppl_gpt-345m': 42.22, 'lamb

AssistantStep 8submit

Tool Input

1{
2  "n": 2
3}

Tool ResultStep 8

[submit] Submitting result from test #2 as final.

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6771, val_loss=2.3969
TRAIN_METRICS: step=9000, train_loss=2.6470, val_loss=2.3702
TRAIN_METRICS: step=10000, train_loss=2.6311, val_loss=2.3530
TRAIN_METRICS: step=11000, train_loss=2.5653, val_loss=2.2849
TRAIN_METRICS: step=12000, train_loss=2.5278, val_loss=2.2510
Final metrics (gpt-345m): val_loss_gpt-345m=2.2433, wikitext2_ppl_gpt-345m=41.6000, lambada_ppl_gpt-345m=64.2200

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.9900, hellaswag_lm-eval-345m=34.1700, piqa_lm-eval-345m=63.6600, winogrande_lm-eval-345m=50.5900

[submit] Finalized test #2 as final.

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2433, 'wikitext2_ppl_gpt-345m': 41.6, 'lambada_ppl_gpt-345m': 64.22, 'elapsed_gpt-345m': 20353, 'arc_easy_lm-eval-345m': 56.99, 'hellaswag_lm-eval-345m': 34.17, 'piqa_lm-eval