Agent Conversation: gpt-5.4

Model: gpt-5.4

Total steps: 7

Messages: 16

UserStep 0

Task: llm-pretrain-embedding

LLM Pretraining: Embedding Strategy Optimization

Research Question

Design an improved embedding strategy for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to standard token + position embeddings with weight tying.

What You Can Modify

The TokenEmbedding class (lines 116-140) in custom_pretrain.py:

Token embedding representation (default: learned token + position embeddings)
Weight tying strategy (default: input embedding = output lm_head weight)
Additional embedding sources (e.g., n-gram embeddings, hash-based embeddings)
Value embeddings that inject into transformer layers via get_value_embed(layer_idx)

Interface: Your TokenEmbedding class must implement:

forward(idx) -> x: Takes token indices (B, T), returns embeddings (B, T, n_embd)
get_lm_head_weight() -> weight: Returns the weight tensor for the output projection
get_num_pos_params() -> int: Returns count of position parameters (excluded from reported param count)
get_value_embed(layer_idx) -> Optional[Tensor]: (Optional) Returns per-layer value embedding residual (B, T, n_embd) or None

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 115–140, lines 265–267 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 CausalSelfAtten

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 115,
5  "end_line": 140,
6  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"Hybrid factorized token embedding with gated hashed bigram residuals.\n\n    Algorithmic idea:\n    1) keep tied input/output embeddings, but parameterize them as a low-rank\n       lexical embedding plus a learned token-specific gate over a shared basis;\n    2) add a lightweight hashed bigram residual at the input and in a few deeper\n       layers, scaled by confidence gates derived from the current token.\n\n    This aims to improve lexical sharing and local compositionality without\n    changing transformer width/depth.\n    \"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n        self.drop = nn.Dropout(config.dropout)\n\n        # Factorized tied token embedding: token coefficients times shared basis.\n        self.rank = max(64, config.n_embd // 8)\n        self.token_coef = nn.Embedding(config.vocab_size, self.rank)\n        self.token_gate = nn.Embedding(config.vocab_size, self.rank)\n        self.shared_basis = nn.Parameter(torch.empty(self.rank, config.n_embd))\n\n        # Position embedding kept standard for compatibility.\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n\n        # Lightweight hashed bigram table for local lexical interactions.\n        self.bigram_vocab_size = config.vocab_size * 2\n        self.bigram_embed = nn.Embedding(self.bigram_vocab_size, config.n_embd)\n        self.input_bigram_scale = nn.Parameter(torch.tensor(0.10))\n        self.deep_bigram_scale = nn.Parameter(torch.tensor(0.06))\n\n        # Token-dependent confidence gate controls how much bigram signal enters.\n        self.bigram_conf = nn.Embedding(config.vocab_size, 1)\n\n        # Inject into a small set of deeper layers only.\n        self._inject_layers = sorted(set([0, max(0, self.n_layer // 2), self.n_layer - 1]))\n        self._cached_value_embeds = None\n\n        nn.init.normal_(self.shared_basis, mean=0.0, std=0.02)\n        nn.init.normal_(self.token_coef.weight, mean=0.0, std=0.02)\n        nn.init.zeros_(self.token_gate.weight)\n        nn.init.zeros_(self.bigram_embed.weight)\n        nn.init.zeros_(self.bigram_conf.weight)\n\n    def _token_embed(self, idx):\n        coef = self.token_coef(idx)\n        gate = 1.0 + 0.5 * torch.tanh(self.token_gate(idx))\n        return (coef * gate) @ self.shared_basis\n\n    def _bigram_hash(self, idx):\n        x = idx.to(torch.int64)\n        mod = self.bigram_vocab_size - 1\n        out = torch.zeros_like(x)\n        out[:, 0] = mod\n        out[:, 1:] = ((x[:, 1:] * 1315423911) ^ (x[:, :-1] * 2654435761)) % mod\n        return out.long()\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self._token_embed(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n\n        bigram = self.bigram_embed(self._bigram_hash(idx))\n        conf = torch.sigmoid(self.bigram_conf(idx))\n        bigram = conf * bigram\n        self._cached_value_embeds = {\n            layer_idx: self.deep_bigram_scale * bigram for layer_idx in self._inject_layers\n        }\n        return self.drop(tok_emb + pos_emb + self.input_bigram_scale * bigram)\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_value_embeds is None:\n            return None\n        return self._cached_value_embeds.get(layer_idx, None)\n\n    def get_lm_head_weight(self):\n        gate = 1.0 + 0.5 * torch.tanh(self.token_gate.weight)\n        return (self.token_coef.weight * gate) @ self.shared_basis\n\n    def get_num_pos_params(self):\n        return self.wpe.weight.numel()"
7}

Tool ResultStep 1

OK: Replaced lines 115..140 with 86 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–200, 325–327.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–200, 325–327 | total: 533 lines]
   115  class TokenEmbedding(nn.Module):
   116      """Hybrid factorized token embedding with gated hashed bigram residuals.
   117  
       ... (80 more lines) ...
   198  
   199      def get_num_pos_params(self):
   200          return self.wpe.weight.numel()
...
   325      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   326      # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
   327      CONFIG_OVERRIDES = {}

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### gpt-345m (scripts/gpt_345m.sh)
[COMMAND FAILED exit=1]
W0415 13:03:16.883000 235462 site-packages/torch/distributed/run.py:792] 
W0415 13:03:16.883000 235462 site-packages/torch/distributed/run.py:792] *****************************************
W0415 13:03:16.883000 235462 site-packages/torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0415 13:03:16.883000 235462 site-packages/torch/distributed/run.py:792] *****************************************
[W415 13:03:16.960443144 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3
[W415 13:03:16.960737329 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3
[W415 13:03:17.261558522 socket.cpp:20

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 115,
5  "end_line": 200,
6  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"Adaptive dual-lexicon embedding with gated hashed bigram residuals.\n\n    Novelty:\n    - Maintain standard tied embedding as an anchor for stable optimization.\n    - Add a second full lexical table that is blended per token through a learned\n      gate, yielding token-dependent partial untying while preserving a tied\n      lm_head parameter interface.\n    - Add a lightweight hashed bigram residual both at input and selected deep\n      layers, with token-conditioned confidence gating.\n    \"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.alt_wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.mix_gate = nn.Embedding(config.vocab_size, 1)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n\n        self.bigram_vocab_size = config.vocab_size * 2\n        self.bigram_embed = nn.Embedding(self.bigram_vocab_size, config.n_embd)\n        self.bigram_conf = nn.Embedding(config.vocab_size, 1)\n        self.input_bigram_scale = nn.Parameter(torch.tensor(0.08))\n        self.deep_bigram_scale = nn.Parameter(torch.tensor(0.05))\n        self._inject_layers = sorted(set([1, max(1, config.n_layer // 2), config.n_layer - 1]))\n        self._cached_value_embeds = None\n\n        nn.init.normal_(self.alt_wte.weight, mean=0.0, std=0.02)\n        nn.init.zeros_(self.mix_gate.weight)\n        nn.init.zeros_(self.bigram_embed.weight)\n        nn.init.zeros_(self.bigram_conf.weight)\n\n    def _bigram_hash(self, idx):\n        x = idx.to(torch.int64)\n        mod = self.bigram_vocab_size - 1\n        out = torch.zeros_like(x)\n        out[:, 0] = mod\n        out[:, 1:] = ((x[:, 1:] * 65537) + (x[:, :-1] * 8191)) % mod\n        return out.long()\n\n    def _mixed_token_embed(self, idx):\n        gate = torch.sigmoid(self.mix_gate(idx))\n        base = self.wte(idx)\n        alt = self.alt_wte(idx)\n        return base + gate * (alt - base)\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self._mixed_token_embed(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n\n        bigram = self.bigram_embed(self._bigram_hash(idx))\n        conf = torch.sigmoid(self.bigram_conf(idx))\n        gated_bigram = conf * bigram\n        self._cached_value_embeds = {\n            layer_idx: self.deep_bigram_scale * gated_bigram for layer_idx in self._inject_layers\n        }\n        return self.drop(tok_emb + pos_emb + self.input_bigram_scale * gated_bigram)\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_value_embeds is None:\n            return None\n        return self._cached_value_embeds.get(layer_idx, None)\n\n    def get_lm_head_weight(self):\n        gate = torch.sigmoid(self.mix_gate.weight)\n        mixed = self.wte.weight + gate * (self.alt_wte.weight - self.wte.weight)\n        return nn.Parameter(mixed)\n\n    def get_num_pos_params(self):\n        return self.wpe.weight.numel()"
7}

Tool ResultStep 3

OK: Replaced lines 115..200 with 76 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–190, 315–317.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–190, 315–317 | total: 523 lines]
   115  class TokenEmbedding(nn.Module):
   116      """Adaptive dual-lexicon embedding with gated hashed bigram residuals.
   117  
       ... (70 more lines) ...
   188  
   189      def get_num_pos_params(self):
   190          return self.wpe.weight.numel()
...
   315      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   316      # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
   317      CONFIG_OVERRIDES = {}

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=8000, train_loss=2.6466, val_loss=2.3673
TRAIN_METRICS: step=9000, train_loss=2.6158, val_loss=2.3324
TRAIN_METRICS: step=10000, train_loss=2.5944, val_loss=2.3186
TRAIN_METRICS: step=11000, train_loss=2.5841, val_loss=2.3223
TRAIN_METRICS: step=12000, train_loss=2.5662, val_loss=2.3010
Final metrics (gpt-345m): val_loss_gpt-345m=2.2993, wikitext2_ppl_gpt-345m=45.3100, lambada_ppl_gpt-345m=69.3400

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.0900, hellaswag_lm-eval-345m=33.3000, piqa_lm-eval-345m=64.2000, winogrande_lm-eval-345m=51.4600

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2993, 'wikitext2_ppl_gpt-345m': 45.31, 'lambada_ppl_gpt-345m': 69.34, 'elapsed_gpt-345m': 20548, 'arc_easy_lm-eval-345m': 55.09, 'hellaswag_lm-eval-345m': 33.3, 'piqa_l

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 115,
5  "end_line": 190,
6  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"Adaptive dual-lexicon embedding with depth-decayed hashed n-gram residuals.\n\n    Core idea:\n    - Start from a tied lexical table wte.\n    - Learn a token-wise gated residual lexical table alt_wte, creating partial\n      untying while still exposing a single mixed lm_head weight.\n    - Add a convex mixture of hashed bigram and trigram embeddings. The n-gram\n      signal is injected strongly at the input and more weakly into selected\n      layers with depth-dependent decay, improving local compositional modeling.\n    \"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.alt_wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.mix_gate = nn.Embedding(config.vocab_size, 1)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n\n        self.ngram_vocab_size = config.vocab_size * 2\n        self.bigram_embed = nn.Embedding(self.ngram_vocab_size, config.n_embd)\n        self.trigram_embed = nn.Embedding(self.ngram_vocab_size, config.n_embd)\n        self.ngram_conf = nn.Embedding(config.vocab_size, 1)\n        self.ngram_mix = nn.Embedding(config.vocab_size, 1)\n        self.input_ngram_scale = nn.Parameter(torch.tensor(0.10))\n        self.layer_scales = nn.Parameter(torch.tensor([0.050, 0.035, 0.020]))\n        self._inject_layers = sorted(set([1, max(1, config.n_layer // 2), config.n_layer - 1]))\n        self._cached_value_embeds = None\n\n        nn.init.normal_(self.alt_wte.weight, mean=0.0, std=0.02)\n        nn.init.zeros_(self.mix_gate.weight)\n        nn.init.zeros_(self.bigram_embed.weight)\n        nn.init.zeros_(self.trigram_embed.weight)\n        nn.init.zeros_(self.ngram_conf.weight)\n        nn.init.zeros_(self.ngram_mix.weight)\n\n    def _bigram_hash(self, idx):\n        x = idx.to(torch.int64)\n        mod = self.ngram_vocab_size - 1\n        out = torch.zeros_like(x)\n        out[:, 0] = mod\n        out[:, 1:] = ((x[:, 1:] * 65537) + (x[:, :-1] * 8191)) % mod\n        return out.long()\n\n    def _trigram_hash(self, idx):\n        x = idx.to(torch.int64)\n        mod = self.ngram_vocab_size - 1\n        out = torch.zeros_like(x)\n        out[:, :2] = mod\n        out[:, 2:] = ((x[:, 2:] * 131071) + (x[:, 1:-1] * 4099) + (x[:, :-2] * 257)) % mod\n        return out.long()\n\n    def _mixed_token_embed(self, idx):\n        gate = torch.sigmoid(self.mix_gate(idx))\n        base = self.wte(idx)\n        alt = self.alt_wte(idx)\n        return base + gate * (alt - base)\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self._mixed_token_embed(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n\n        conf = torch.sigmoid(self.ngram_conf(idx))\n        mix = torch.sigmoid(self.ngram_mix(idx))\n        bigram = self.bigram_embed(self._bigram_hash(idx))\n        trigram = self.trigram_embed(self._trigram_hash(idx))\n        ngram = conf * (mix * trigram + (1.0 - mix) * bigram)\n\n        self._cached_value_embeds = {\n            layer_idx: self.layer_scales[i] * ngram for i, layer_idx in enumerate(self._inject_layers)\n        }\n        return self.drop(tok_emb + pos_emb + self.input_ngram_scale * ngram)\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_value_embeds is None:\n            return None\n        return self._cached_value_embeds.get(layer_idx, None)\n\n    def get_lm_head_weight(self):\n        gate = torch.sigmoid(self.mix_gate.weight)\n        mixed = self.wte.weight + gate * (self.alt_wte.weight - self.wte.weight)\n        return nn.Parameter(mixed)\n\n    def get_num_pos_params(self):\n        return self.wpe.weight.numel()"
7}

Tool ResultStep 5

OK: Replaced lines 115..190 with 91 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–205, 330–332.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–205, 330–332 | total: 538 lines]
   115  class TokenEmbedding(nn.Module):
   116      """Adaptive dual-lexicon embedding with depth-decayed hashed n-gram residuals.
   117  
       ... (85 more lines) ...
   203  
   204      def get_num_pos_params(self):
   205          return self.wpe.weight.numel()
...
   330      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   331      # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
   332      CONFIG_OVERRIDES = {}

AssistantStep 6test

Tool ResultStep 6

[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=2.6437, val_loss=2.3643
TRAIN_METRICS: step=9000, train_loss=2.6077, val_loss=2.3459
TRAIN_METRICS: step=10000, train_loss=2.5930, val_loss=2.3312
TRAIN_METRICS: step=11000, train_loss=2.5658, val_loss=2.3100
TRAIN_METRICS: step=12000, train_loss=2.5728, val_loss=2.3053
Final metrics (gpt-345m): val_loss_gpt-345m=2.3025, wikitext2_ppl_gpt-345m=45.8600, lambada_ppl_gpt-345m=70.7000

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.0600, hellaswag_lm-eval-345m=33.1800, piqa_lm-eval-345m=64.3600, winogrande_lm-eval-345m=52.1700

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3025, 'wikitext2_ppl_gpt-345m': 45.86, 'lamb

AssistantStep 7submit

Tool Input

1{
2  "n": 2
3}

Tool ResultStep 7

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

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6466, val_loss=2.3673
TRAIN_METRICS: step=9000, train_loss=2.6158, val_loss=2.3324
TRAIN_METRICS: step=10000, train_loss=2.5944, val_loss=2.3186
TRAIN_METRICS: step=11000, train_loss=2.5841, val_loss=2.3223
TRAIN_METRICS: step=12000, train_loss=2.5662, val_loss=2.3010
Final metrics (gpt-345m): val_loss_gpt-345m=2.2993, wikitext2_ppl_gpt-345m=45.3100, lambada_ppl_gpt-345m=69.3400

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.0900, hellaswag_lm-eval-345m=33.3000, piqa_lm-eval-345m=64.2000, winogrande_lm-eval-345m=51.4600

[submit] Finalized test #2 as final.

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2993, 'wikitext2_ppl_gpt-345m': 45.31, 'lambada_ppl_gpt-345m': 69.34, 'elapsed_gpt-345m': 20548, 'arc_easy_lm-eval-345m': 55.09, 'hellaswag_lm-eval-345m': 33.3, 'piqa_lm-eval