Agent Conversation: deepseek-reasoner
Model: deepseek-reasoner
Total steps: 11
Messages: 27
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 \"\"\"Compute language modeling loss with label smoothing, z-loss, and adaptive temperature scaling.\"\"\"\n B, T, V = logits.shape\n flat_logits = logits.view(-1, V)\n flat_targets = targets.view(-1)\n \n # Standard cross-entropy for evaluation\n if not torch.is_grad_enabled():\n return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1)\n \n # Training: apply adaptive temperature scaling\n # Compute per-token logit standard deviation across vocabulary\n logit_std = flat_logits.std(dim=-1, keepdim=True) # shape (B*T, 1)\n # Temperature = 1 / (1 + std), detached from gradient graph\n temperature = 1.0 / (1.0 + logit_std.detach())\n scaled_logits = flat_logits * temperature\n \n # Label smoothing with epsilon = 0.05\n eps = 0.05\n # Create smoothed target distribution\n with torch.no_grad():\n true_dist = torch.zeros_like(scaled_logits)\n true_dist.fill_(eps / (V - 1))\n true_dist.scatter_(1, flat_targets.unsqueeze(1), 1 - eps)\n # Mask out ignored positions (target == -1)\n ignore_mask = (flat_targets == -1).unsqueeze(1)\n true_dist.masked_fill_(ignore_mask, 0.0)\n \n # Smoothed cross-entropy loss\n smoothed_ce = -(true_dist * F.log_softmax(scaled_logits, dim=-1)).sum(dim=-1)\n smoothed_ce = smoothed_ce * (flat_targets != -1).float()\n smoothed_ce = smoothed_ce.mean()\n \n # Z-loss regularization: penalize large log-partition values\n log_z = torch.logsumexp(scaled_logits, dim=-1)\n z_loss = (log_z ** 2).mean() * 0.01 # weight\n \n total_loss = smoothed_ce + z_loss\n return total_loss"
7}Tool ResultStep 1
OK: Replaced lines 188..191 with 39 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–226, 282–284. [Current file: nanoGPT/custom_pretrain.py | editable: 188–226 | total: 474 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """Compute language modeling loss with label smoothing, z-loss, and adaptive temperature scaling.""" 190 B, T, V = logits.shape 191 flat_logits = logits.view(-1, V) 192 flat_targets = targets.view(-1) 193 194 # Standard cross-entropy for evaluation 195 if not torch.is_grad_enabled(): 196 return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1) 197 198 # Training: apply adaptive temperature scaling 199 # Compute per-token logit standard deviation across vocabulary 200 logit_std = flat_logits.std(dim=-1, keepdim=True)
AssistantStep 2edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 188,
5 "end_line": 226,
6 "content": "def compute_loss(logits, targets):\n \"\"\"Compute language modeling loss with label smoothing, z-loss, and focal weighting.\"\"\"\n B, T, V = logits.shape\n flat_logits = logits.view(-1, V)\n flat_targets = targets.view(-1)\n \n # Standard cross-entropy for evaluation (comparable across methods)\n if not torch.is_grad_enabled():\n return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1)\n \n # Training: apply combined loss\n \n # 1. Label smoothing (epsilon = 0.05)\n eps = 0.05\n # Create smoothed target distribution\n with torch.no_grad():\n true_dist = torch.zeros_like(flat_logits)\n true_dist.fill_(eps / (V - 1))\n true_dist.scatter_(1, flat_targets.unsqueeze(1), 1 - eps)\n # Mask out ignored positions (target == -1)\n ignore_mask = (flat_targets == -1).unsqueeze(1)\n true_dist.masked_fill_(ignore_mask, 0.0)\n \n # Smoothed cross-entropy loss\n log_probs = F.log_softmax(flat_logits, dim=-1)\n smoothed_ce = -(true_dist * log_probs).sum(dim=-1)\n # Mask ignored tokens\n non_ignore = (flat_targets != -1).float()\n smoothed_ce = smoothed_ce * non_ignore\n smoothed_ce = smoothed_ce.sum() / (non_ignore.sum() + 1e-8)\n \n # 2. Z-loss regularization (weight = 0.01)\n log_z = torch.logsumexp(flat_logits, dim=-1)\n # Mask ignored tokens\n z_loss = (log_z ** 2) * non_ignore\n z_loss = z_loss.sum() / (non_ignore.sum() + 1e-8)\n z_loss = z_loss * 0.01\n \n # 3. Focal weighting (gamma = 2.0, weight = 0.1)\n # Compute probability of true class\n probs = F.softmax(flat_logits, dim=-1)\n p_t = probs.gather(1, flat_targets.unsqueeze(1)).squeeze(1)\n # Focal factor: (1 - p_t)^gamma\n gamma = 2.0\n focal_weight = (1 - p_t) ** gamma\n focal_weight = focal_weight.detach() # no gradient through weight\n # Cross-entropy per token\n ce_per_token = F.cross_entropy(flat_logits, flat_targets, ignore_index=-1, reduction='none')\n focal_loss = (focal_weight * ce_per_token) * non_ignore\n focal_loss = focal_loss.sum() / (non_ignore.sum() + 1e-8)\n focal_loss = focal_loss * 0.1\n \n total_loss = smoothed_ce + z_loss + focal_loss\n return total_loss"
7}Tool ResultStep 2
OK: Replaced lines 188..226 with 54 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–241, 297–299. [Current file: nanoGPT/custom_pretrain.py | editable: 188–241 | total: 489 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """Compute language modeling loss with label smoothing, z-loss, and focal weighting.""" 190 B, T, V = logits.shape 191 flat_logits = logits.view(-1, V) 192 flat_targets = targets.view(-1) 193 194 # Standard cross-entropy for evaluation (comparable across methods) 195 if not torch.is_grad_enabled(): 196 return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1) 197 198 # Training: apply combined loss 199 200 # 1. Label smoothing (epsilon = 0.05) 201 eps = 0.05 202 # Create smoothed target distribution
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=9000, train_loss=2.6874, val_loss=2.4119
TRAIN_METRICS: step=10000, train_loss=2.6657, val_loss=2.3846
TRAIN_METRICS: step=11000, train_loss=2.6475, val_loss=2.3753
TRAIN_METRICS: step=12000, train_loss=2.6276, val_loss=2.3565
TRAIN_METRICS: step=13000, train_loss=2.6126, val_loss=2.3473
Final metrics (gpt-345m): val_loss_gpt-345m=2.3408, wikitext2_ppl_gpt-345m=46.9500, lambada_ppl_gpt-345m=71.3200
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.1800, hellaswag_lm-eval-345m=33.3500, piqa_lm-eval-345m=64.2500, winogrande_lm-eval-345m=50.5900
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3408, 'wikitext2_ppl_gpt-345m': 46.95, 'lambada_ppl_gpt-345m': 71.32, 'elapsed_gpt-345m': 20928, 'arc_easy_lm-eval-345m': 55.18, 'hellaswag_lm-eval-345m': 33.35, 'piqa_lm-eval-345m': 64.25, '
AssistantStep 4edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 188,
5 "end_line": 241,
6 "content": "def compute_loss(logits, targets):\n \"\"\"Compute language modeling loss with label smoothing, z-loss, and confidence penalty.\"\"\"\n B, T, V = logits.shape\n flat_logits = logits.view(-1, V)\n flat_targets = targets.view(-1)\n \n # Standard cross-entropy for evaluation (comparable across methods)\n if not torch.is_grad_enabled():\n return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1)\n \n # Training: apply combined loss\n \n # 1. Label smoothing (epsilon = 0.05)\n eps = 0.05\n # Create smoothed target distribution\n with torch.no_grad():\n true_dist = torch.zeros_like(flat_logits)\n true_dist.fill_(eps / (V - 1))\n true_dist.scatter_(1, flat_targets.unsqueeze(1), 1 - eps)\n # Mask out ignored positions (target == -1)\n ignore_mask = (flat_targets == -1).unsqueeze(1)\n true_dist.masked_fill_(ignore_mask, 0.0)\n \n # Smoothed cross-entropy loss\n log_probs = F.log_softmax(flat_logits, dim=-1)\n smoothed_ce = -(true_dist * log_probs).sum(dim=-1)\n # Mask ignored tokens\n non_ignore = (flat_targets != -1).float()\n smoothed_ce = smoothed_ce * non_ignore\n smoothed_ce = smoothed_ce.sum() / (non_ignore.sum() + 1e-8)\n \n # 2. Z-loss regularization (weight = 0.01)\n log_z = torch.logsumexp(flat_logits, dim=-1)\n # Mask ignored tokens\n z_loss = (log_z ** 2) * non_ignore\n z_loss = z_loss.sum() / (non_ignore.sum() + 1e-8)\n z_loss = z_loss * 0.01\n \n # 3. Confidence penalty: encourage higher entropy (weight = 0.1)\n probs = F.softmax(flat_logits, dim=-1)\n entropy = -(probs * torch.log(probs + 1e-8)).sum(dim=-1)\n # We want to penalize low entropy (overconfidence), so subtract entropy from loss\n # i.e., add negative entropy (since loss is minimized)\n conf_penalty = -entropy * non_ignore\n conf_penalty = conf_penalty.sum() / (non_ignore.sum() + 1e-8)\n conf_penalty = conf_penalty * 0.1\n \n total_loss = smoothed_ce + z_loss + conf_penalty\n return total_loss"
7}Tool ResultStep 4
OK: Replaced lines 188..241 with 49 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–236, 292–294. [Current file: nanoGPT/custom_pretrain.py | editable: 188–236 | total: 484 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """Compute language modeling loss with label smoothing, z-loss, and confidence penalty.""" 190 B, T, V = logits.shape 191 flat_logits = logits.view(-1, V) 192 flat_targets = targets.view(-1) 193 194 # Standard cross-entropy for evaluation (comparable across methods) 195 if not torch.is_grad_enabled(): 196 return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1) 197 198 # Training: apply combined loss 199 200 # 1. Label smoothing (epsilon = 0.05) 201 eps = 0.05 202 # Create smoothed target distributi
AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 292,
5 "end_line": 294,
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 = {'learning_rate': 6.6e-4}"
7}Tool ResultStep 5
OK: Replaced lines 292..294 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–236, 292–294. [Current file: nanoGPT/custom_pretrain.py | editable: 188–236 | total: 484 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """Compute language modeling loss with label smoothing, z-loss, and confidence penalty.""" 190 B, T, V = logits.shape 191 flat_logits = logits.view(-1, V) 192 flat_targets = targets.view(-1) 193 194 # Standard cross-entropy for evaluation (comparable across methods) 195 if not torch.is_grad_enabled(): 196 return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1) 197 198 # Training: apply combined loss 199 200 # 1. Label smoothing (epsilon = 0.05) 201 eps = 0.05 202 # Create smoothed target distributio
AssistantStep 6test
Tool ResultStep 6
[Test #2] (1 test remaining; call submit(n=2) to submit this result)
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=0, train_loss=11.0307, val_loss=11.0299
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.1800, hellaswag_lm-eval-345m=33.3500, piqa_lm-eval-345m=64.2500, winogrande_lm-eval-345m=50.5900
[Leaderboard] Results saved: {'elapsed_gpt-345m': 1427, 'arc_easy_lm-eval-345m': 55.18, 'hellaswag_lm-eval-345m': 33.35, 'piqa_lm-eval-345m': 64.25, 'winogrande_lm-eval-345m': 50.59, 'elapsed_lm-eval-345m': 385}AssistantStep 7edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 292,
5 "end_line": 294,
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 = {}"
7}Tool ResultStep 7
OK: Replaced lines 292..294 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–236, 292–294. [Current file: nanoGPT/custom_pretrain.py | editable: 188–236 | total: 484 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """Compute language modeling loss with label smoothing, z-loss, and confidence penalty.""" 190 B, T, V = logits.shape 191 flat_logits = logits.view(-1, V) 192 flat_targets = targets.view(-1) 193 194 # Standard cross-entropy for evaluation (comparable across methods) 195 if not torch.is_grad_enabled(): 196 return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1) 197 198 # Training: apply combined loss 199 200 # 1. Label smoothing (epsilon = 0.05) 201 eps = 0.05 202 # Create smoothed target distributio
AssistantStep 8edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 188,
5 "end_line": 236,
6 "content": "def compute_loss(logits, targets):\n \"\"\"Compute language modeling loss with label smoothing and z-loss.\"\"\"\n B, T, V = logits.shape\n flat_logits = logits.view(-1, V)\n flat_targets = targets.view(-1)\n \n # Standard cross-entropy for evaluation (comparable across methods)\n if not torch.is_grad_enabled():\n return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1)\n \n # Training: apply combined loss\n \n # 1. Label smoothing (epsilon = 0.05)\n eps = 0.05\n # Create smoothed target distribution\n with torch.no_grad():\n true_dist = torch.zeros_like(flat_logits)\n true_dist.fill_(eps / (V - 1))\n true_dist.scatter_(1, flat_targets.unsqueeze(1), 1 - eps)\n # Mask out ignored positions (target == -1)\n ignore_mask = (flat_targets == -1).unsqueeze(1)\n true_dist.masked_fill_(ignore_mask, 0.0)\n \n # Smoothed cross-entropy loss\n log_probs = F.log_softmax(flat_logits, dim=-1)\n smoothed_ce = -(true_dist * log_probs).sum(dim=-1)\n # Mask ignored tokens\n non_ignore = (flat_targets != -1).float()\n smoothed_ce = smoothed_ce * non_ignore\n smoothed_ce = smoothed_ce.sum() / (non_ignore.sum() + 1e-8)\n \n # 2. Z-loss regularization (weight = 0.01)\n log_z = torch.logsumexp(flat_logits, dim=-1)\n # Mask ignored tokens\n z_loss = (log_z ** 2) * non_ignore\n z_loss = z_loss.sum() / (non_ignore.sum() + 1e-8)\n z_loss = z_loss * 0.01\n \n total_loss = smoothed_ce + z_loss\n return total_loss"
7}Tool ResultStep 8
OK: Replaced lines 188..236 with 40 line(s) in nanoGPT/custom_pretrain.py. Editable range: 188–227, 283–285. [Current file: nanoGPT/custom_pretrain.py | editable: 188–227 | total: 475 lines] 185| return optimizer 186| 187| # ── Loss Computation ─────────────────────────────────────────────────────── 188 def compute_loss(logits, targets): 189 """Compute language modeling loss with label smoothing and z-loss.""" 190 B, T, V = logits.shape 191 flat_logits = logits.view(-1, V) 192 flat_targets = targets.view(-1) 193 194 # Standard cross-entropy for evaluation (comparable across methods) 195 if not torch.is_grad_enabled(): 196 return F.cross_entropy(flat_logits, flat_targets, ignore_index=-1) 197 198 # Training: apply combined loss 199 200 # 1. Label smoothing (epsilon = 0.05) 201 eps = 0.05 202 # Create smoothed target distribution 203 with t
AssistantStep 9test
AssistantStep 9edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 283,
5 "end_line": 285,
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 = {}"
7}