Agent Conversation: claude-opus-4.6
Model: claude-opus-4.6
Total steps: 5
Messages: 13
UserStep 0
Task: llm-pretrain-loss
LLM Pretraining: Loss Function Optimization
Research Question
Design an improved loss function for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to standard cross-entropy.
What You Can Modify
The compute_loss function (lines 189-191) in custom_pretrain.py:
- Loss function formulation (default: standard cross-entropy)
- Logit processing (e.g., softcapping, temperature scaling)
- Regularization terms (e.g., z-loss, entropy penalties)
- Label distribution modifications (e.g., label smoothing)
Note: The function signature compute_loss(logits, targets) must be preserved. logits has shape (B, T, V) and targets has shape (B, T). The function is called inside the model's forward pass during training.
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 188–191, lines 247–249 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
AssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 188,
5 "end_line": 191,
6 "content": "def compute_loss(logits, targets):\n \"\"\"Enhanced cross-entropy with PolyLoss-1 correction and z-loss.\n\n Novel combination for LLM pretraining:\n - Label smoothing (0.05): distributional regularization\n - PolyLoss-1 (eps=1): amplifies gradients for partially-learned tokens\n by adding eps*(1-p_target), modifying gradient to (p_t-1)*(1+eps*p_t)\n - Z-loss (1e-4): stabilizes the log-partition function\n Standard CE during evaluation for fair comparison.\n \"\"\"\n flat_logits = logits.view(-1, logits.size(-1))\n flat_targets = targets.view(-1)\n if not torch.is_grad_enabled():\n return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1)\n # Label-smoothed cross-entropy\n ce = F.cross_entropy(flat_logits, flat_targets, ignore_index=-1, label_smoothing=0.05)\n # PolyLoss-1: eps*(1 - p_target) for enhanced gradient signal\n logits_f = flat_logits.float()\n log_z = torch.logsumexp(logits_f, dim=-1)\n tgt_logit = logits_f.gather(1, flat_targets.unsqueeze(1)).squeeze(1)\n pt = (tgt_logit - log_z).exp()\n poly = (1.0 - pt).mean()\n # Z-loss: penalize large log-partition values for stability\n z_loss = 1e-4 * (log_z ** 2).mean()\n return ce + poly + z_loss\n\n"
7}Tool ResultStep 1
OK: Replaced lines 188..191 with 26 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–213, 269–271. [Current file: nanoGPT/custom_pretrain.py | editable: 188–213 | total: 461 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """Enhanced cross-entropy with PolyLoss-1 correction and z-loss. 190 191 Novel combination for LLM pretraining: 192 - Label smoothing (0.05): distributional regularization 193 - PolyLoss-1 (eps=1): amplifies gradients for partially-learned tokens 194 by adding eps*(1-p_target), modifying gradient to (p_t-1)*(1+eps*p_t) 195 - Z-loss (1e-4): stabilizes the log-partition function 196 Standard CE during evaluation for fair comparison. 197 """ 198 flat_logits = logits.view(-1, logits.size(-1)) 199 flat_targets = targets.view(-1) 200 if n
AssistantStep 2test
Tool ResultStep 2
[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=9000, train_loss=2.6867, val_loss=2.4113
TRAIN_METRICS: step=10000, train_loss=2.6657, val_loss=2.3838
TRAIN_METRICS: step=11000, train_loss=2.6474, val_loss=2.3741
TRAIN_METRICS: step=12000, train_loss=2.6269, val_loss=2.3552
TRAIN_METRICS: step=13000, train_loss=2.6121, val_loss=2.3455
Final metrics (gpt-345m): val_loss_gpt-345m=2.3383, wikitext2_ppl_gpt-345m=47.9100, lambada_ppl_gpt-345m=72.3000
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=53.2000, hellaswag_lm-eval-345m=33.2000, piqa_lm-eval-345m=63.8200, winogrande_lm-eval-345m=50.9100
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3383, 'wikitext2_ppl_gpt-345m': 47.91, 'lambada_ppl_gpt-345m': 72.3, 'elapsed_gpt-345m': 20878, 'arc_easy_lm-eval-345m': 53.2, 'hellaswag_lm-eval-345m': 33.2, 'piqa_lm-eval-345m': 63.82, 'win
AssistantStep 3edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 188,
5 "end_line": 213,
6 "content": "def compute_loss(logits, targets):\n \"\"\"Multi-regularized cross-entropy: tanh softcap + label smoothing + z-loss.\n\n Novel combination addressing three distinct failure modes in LLM pretraining:\n 1. Tanh softcap (cap=30) bounds logit magnitudes, preventing gradient\n instability from extreme logit values (inspired by Gemma 2)\n 2. Label smoothing (eps=0.1) redistributes probability mass, preventing\n overconfident predictions and improving generalization\n 3. Z-loss (1e-4) penalizes large log-partition values, stabilizing the\n log-softmax computation (inspired by PaLM)\n Standard CE used during evaluation for fair comparison.\n \"\"\"\n flat_logits = logits.view(-1, logits.size(-1))\n flat_targets = targets.view(-1)\n if not torch.is_grad_enabled():\n return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1)\n # Tanh-based logit softcapping: bounds to [-cap, cap]\n cap = 30.0\n capped = cap * torch.tanh(flat_logits.float() / cap)\n # Cross-entropy with label smoothing\n ce = F.cross_entropy(capped, flat_targets, ignore_index=-1, label_smoothing=0.1)\n # Z-loss: penalize large log-partition values for stability\n log_z = torch.logsumexp(capped, dim=-1)\n z_loss = 1e-4 * (log_z ** 2).mean()\n return ce + z_loss\n\n"
7}Tool ResultStep 3
OK: Replaced lines 188..213 with 26 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–213, 269–271. [Current file: nanoGPT/custom_pretrain.py | editable: 188–213 | total: 461 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """Multi-regularized cross-entropy: tanh softcap + label smoothing + z-loss. 190 191 Novel combination addressing three distinct failure modes in LLM pretraining: 192 1. Tanh softcap (cap=30) bounds logit magnitudes, preventing gradient 193 instability from extreme logit values (inspired by Gemma 2) 194 2. Label smoothing (eps=0.1) redistributes probability mass, preventing 195 overconfident predictions and improving generalization 196 3. Z-loss (1e-4) penalizes large log-partition values, stabilizing the 197 log-softmax computation (inspired by PaL
AssistantStep 4test
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=9000, train_loss=2.6901, val_loss=2.4178
TRAIN_METRICS: step=10000, train_loss=2.6663, val_loss=2.3872
TRAIN_METRICS: step=11000, train_loss=2.6485, val_loss=2.3777
TRAIN_METRICS: step=12000, train_loss=2.6282, val_loss=2.3590
TRAIN_METRICS: step=13000, train_loss=2.6119, val_loss=2.3483
Final metrics (gpt-345m): val_loss_gpt-345m=2.3403, wikitext2_ppl_gpt-345m=47.5100, lambada_ppl_gpt-345m=73.7600
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=53.2800, hellaswag_lm-eval-345m=32.7800, piqa_lm-eval-345m=63.6600, winogrande_lm-eval-345m=49.8000
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3403, 'wikitext2_ppl_gpt-345m': 47.51, 'lambada_ppl_gpt-345m': 73.76, 'elapsed_gpt-345m': 20436, 'arc_easy_lm-eval-345m': 53.28, 'hellaswag_lm-eval-345m': 32.78, 'piqa
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=9000, train_loss=2.6867, val_loss=2.4113
TRAIN_METRICS: step=10000, train_loss=2.6657, val_loss=2.3838
TRAIN_METRICS: step=11000, train_loss=2.6474, val_loss=2.3741
TRAIN_METRICS: step=12000, train_loss=2.6269, val_loss=2.3552
TRAIN_METRICS: step=13000, train_loss=2.6121, val_loss=2.3455
Final metrics (gpt-345m): val_loss_gpt-345m=2.3383, wikitext2_ppl_gpt-345m=47.9100, lambada_ppl_gpt-345m=72.3000
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=53.2000, hellaswag_lm-eval-345m=33.2000, piqa_lm-eval-345m=63.8200, winogrande_lm-eval-345m=50.9100
[submit] Finalized test #1 as final.
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3383, 'wikitext2_ppl_gpt-345m': 47.91, 'lambada_ppl_gpt-345m': 72.3, 'elapsed_gpt-345m': 20878, 'arc_easy_lm-eval-345m': 53.2, 'hellaswag_lm-eval-345m': 33.2, 'piqa_lm-eval-