Agent Conversation: gpt-5.4
Model: gpt-5.4
Total steps: 7
Messages: 16
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), 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 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: AssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 191,
5 "end_line": 201,
6 "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n \"\"\"Warmup-Hold-Delayed-Cosine with polynomial tail-smoothing.\n\n Motivation:\n - GPT pretraining often benefits from spending most of training near the peak LR.\n - A very early cosine decay can under-train later tokens at this compute budget.\n - A pure plateau + abrupt linear tail can decay too aggressively near the end.\n\n Schedule:\n 1) warmup: linear ramp\n 2) hold: constant peak LR until 70% of training\n 3) transition: gentle cosine anneal from 70%->90%\n 4) tail: flattened quadratic approach to min_lr from 90%->100%\n \"\"\"\n if it < warmup_iters:\n return learning_rate * (it + 1) / (warmup_iters + 1)\n if it >= lr_decay_iters:\n return min_lr\n\n hold_end = max(warmup_iters + 1, int(lr_decay_iters * 0.70))\n tail_start = max(hold_end + 1, int(lr_decay_iters * 0.90))\n\n if it < hold_end:\n return learning_rate\n\n if it < tail_start:\n t = (it - hold_end) / (tail_start - hold_end)\n coeff = 0.5 * (1.0 + math.cos(math.pi * t))\n mid_lr = min_lr + 0.35 * (learning_rate - min_lr)\n return mid_lr + coeff * (learning_rate - mid_lr)\n\n t = (it - tail_start) / (lr_decay_iters - tail_start)\n start_tail_lr = min_lr + 0.35 * (learning_rate - min_lr)\n coeff = (1.0 - t) ** 2\n return min_lr + coeff * (start_tail_lr - min_lr)"
7}Tool ResultStep 1
OK: Replaced lines 191..201 with 35 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–225, 269–271.
[Current file: nanoGPT/custom_pretrain.py | editable: 191–225, 269–271 | total: 461 lines]
191 def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
192 """Warmup-Hold-Delayed-Cosine with polynomial tail-smoothing.
193
... (29 more lines) ...
223 start_tail_lr = min_lr + 0.35 * (learning_rate - min_lr)
224 coeff = (1.0 - t) ** 2
225 return min_lr + coeff * (start_tail_lr - min_lr)
...
269 # CONFIG_OVERRIDES: override training hyperparameters for your method.
270 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
271 CONFIG_OVERRIDES = {}AssistantStep 2edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 269,
5 "end_line": 271,
6 "content": "# CONFIG_OVERRIDES: override training hyperparameters for your method.\n # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.\n CONFIG_OVERRIDES = {'warmup_iters': int(max_iters * 0.02), 'min_lr': learning_rate / 20}"
7}Tool ResultStep 2
OK: Replaced lines 269..271 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–225, 269–271.
[Current file: nanoGPT/custom_pretrain.py | editable: 191–225, 269–271 | total: 461 lines]
191 def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
192 """Warmup-Hold-Delayed-Cosine with polynomial tail-smoothing.
193
... (29 more lines) ...
223 start_tail_lr = min_lr + 0.35 * (learning_rate - min_lr)
224 coeff = (1.0 - t) ** 2
225 return min_lr + coeff * (start_tail_lr - min_lr)
...
269 # CONFIG_OVERRIDES: override training hyperparameters for your method.
270 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
271 CONFIG_OVERRIDES = {'warmup_iters': int(max_iters * 0.02), 'min_lr': learning_rate / 20}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=2.6885, val_loss=2.4088
TRAIN_METRICS: step=9000, train_loss=2.6500, val_loss=2.3737
TRAIN_METRICS: step=10000, train_loss=2.6085, val_loss=2.3309
TRAIN_METRICS: step=11000, train_loss=2.5617, val_loss=2.2813
TRAIN_METRICS: step=12000, train_loss=2.5378, val_loss=2.2603
Final metrics (gpt-345m): val_loss_gpt-345m=2.2538, wikitext2_ppl_gpt-345m=41.8200, lambada_ppl_gpt-345m=66.1400
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.5600, hellaswag_lm-eval-345m=34.0300, piqa_lm-eval-345m=64.2000, winogrande_lm-eval-345m=52.1700
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2538, 'wikitext2_ppl_gpt-345m': 41.82, 'lambada_ppl_gpt-345m': 66.14, 'elapsed_gpt-345m': 19964, 'arc_easy_lm-eval-345m': 55.56, 'hellaswag_lm-eval-345m': 34.03, 'piqa
AssistantStep 4edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 191,
5 "end_line": 225,
6 "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n \"\"\"Adaptive 3-phase schedule with sqrt warmup and long cosine tail.\n\n Novelty:\n - concave sqrt warmup reaches useful LR faster than linear warmup,\n reducing under-training early in short-to-mid budget pretraining\n - explicit short hold phase stabilizes optimization near the peak LR\n - long cosine decay over the final 35% avoids both overlong plateau and\n the overly sharp late collapse of linear cooldowns\n \"\"\"\n if it < warmup_iters:\n t = (it + 1) / (warmup_iters + 1)\n return learning_rate * math.sqrt(t)\n if it >= lr_decay_iters:\n return min_lr\n\n hold_end = max(warmup_iters + 1, int(lr_decay_iters * 0.65))\n if it < hold_end:\n return learning_rate\n\n t = (it - hold_end) / (lr_decay_iters - hold_end)\n coeff = 0.5 * (1.0 + math.cos(math.pi * t))\n floor = min_lr + 0.10 * (learning_rate - min_lr)\n return floor + coeff * (learning_rate - floor)"
7}Tool ResultStep 4
OK: Replaced lines 191..225 with 24 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–214, 258–260.
[Current file: nanoGPT/custom_pretrain.py | editable: 191–214, 258–260 | total: 450 lines]
191 def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
192 """Adaptive 3-phase schedule with sqrt warmup and long cosine tail.
193
... (18 more lines) ...
212 coeff = 0.5 * (1.0 + math.cos(math.pi * t))
213 floor = min_lr + 0.10 * (learning_rate - min_lr)
214 return floor + coeff * (learning_rate - floor)
...
258 # CONFIG_OVERRIDES: override training hyperparameters for your method.
259 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
260 CONFIG_OVERRIDES = {'warmup_iters': int(max_iters * 0.02), 'min_lr': learning_rate / 20}AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 258,
5 "end_line": 260,
6 "content": "# CONFIG_OVERRIDES: override training hyperparameters for your method.\n # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.\n CONFIG_OVERRIDES = {'warmup_iters': int(max_iters * 0.03), 'min_lr': learning_rate / 25}"
7}Tool ResultStep 5
OK: Replaced lines 258..260 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–214, 258–260.
[Current file: nanoGPT/custom_pretrain.py | editable: 191–214, 258–260 | total: 450 lines]
191 def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
192 """Adaptive 3-phase schedule with sqrt warmup and long cosine tail.
193
... (18 more lines) ...
212 coeff = 0.5 * (1.0 + math.cos(math.pi * t))
213 floor = min_lr + 0.10 * (learning_rate - min_lr)
214 return floor + coeff * (learning_rate - floor)
...
258 # CONFIG_OVERRIDES: override training hyperparameters for your method.
259 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
260 CONFIG_OVERRIDES = {'warmup_iters': int(max_iters * 0.03), 'min_lr': learning_rate / 25}AssistantStep 6test
Tool ResultStep 6
[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=2.6917, val_loss=2.4109
TRAIN_METRICS: step=9000, train_loss=2.6477, val_loss=2.3742
TRAIN_METRICS: step=10000, train_loss=2.6129, val_loss=2.3370
TRAIN_METRICS: step=11000, train_loss=2.5636, val_loss=2.2854
TRAIN_METRICS: step=12000, train_loss=2.5474, val_loss=2.2722
Final metrics (gpt-345m): val_loss_gpt-345m=2.2645, wikitext2_ppl_gpt-345m=42.1600, lambada_ppl_gpt-345m=66.2000
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=57.1100, hellaswag_lm-eval-345m=33.8500, piqa_lm-eval-345m=62.7900, winogrande_lm-eval-345m=51.7800
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2645, 'wikitext2_ppl_gpt-345m': 42.16, 'lambada_ppl_gpt-345m': 66.2, 'elapsed_gpt-345m': 20264, 'arc_easy_lm-eval-345m': 57.11, 'hellaswag_lm-eval-345m': 33.85, 'piqa_l
AssistantStep 7submit
Tool Input
1{
2 "n": 1
3}Tool ResultStep 7
[submit] Submitting result from test #1 as final.
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6885, val_loss=2.4088
TRAIN_METRICS: step=9000, train_loss=2.6500, val_loss=2.3737
TRAIN_METRICS: step=10000, train_loss=2.6085, val_loss=2.3309
TRAIN_METRICS: step=11000, train_loss=2.5617, val_loss=2.2813
TRAIN_METRICS: step=12000, train_loss=2.5378, val_loss=2.2603
Final metrics (gpt-345m): val_loss_gpt-345m=2.2538, wikitext2_ppl_gpt-345m=41.8200, lambada_ppl_gpt-345m=66.1400
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.5600, hellaswag_lm-eval-345m=34.0300, piqa_lm-eval-345m=64.2000, winogrande_lm-eval-345m=52.1700
[submit] Finalized test #1 as final.
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2538, 'wikitext2_ppl_gpt-345m': 41.82, 'lambada_ppl_gpt-345m': 66.14, 'elapsed_gpt-345m': 19964, 'arc_easy_lm-eval-345m': 55.56, 'hellaswag_lm-eval-345m': 34.03, 'piqa_lm-eva