Agent Conversation: claude-opus-4.6

Model: claude-opus-4.6
Total steps: 8
Messages: 20
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)
  • 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 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 CausalSelfAttention(nn.Module):
    34:     def __init__(self, config):
    35:         super().__init__()
    36:         assert con
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    \"\"\"VE-LCR: Value Embeddings with Local Context Residual.\n    Cyclically injects 5 value embedding tables at all layers (except layer 0),\n    each augmented with a previous-token value residual for local context.\n    Uses raw nn.Parameter (not nn.Embedding) to bypass _init_weights override.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\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        self.n_ve = 5\n        # Raw Parameter avoids _init_weights reinit; controlled small init\n        self._vte = nn.Parameter(torch.randn(config.vocab_size * self.n_ve, config.n_embd) * 0.01)\n        # Per-layer blending: alpha for current token, beta for previous token context\n        self.ve_alpha = nn.Parameter(torch.full((config.n_layer,), 0.5))\n        self.ve_beta = nn.Parameter(torch.full((config.n_layer,), 0.1))\n        self._cached_ve = None\n\n    def forward(self, idx):\n        b, t = idx.size()\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        V = self.vocab_size\n        self._cached_ve = [F.embedding(idx + i * V, self._vte) for i in range(self.n_ve)]\n        return self.drop(self.wte(idx) + self.wpe(pos))\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_ve is None or layer_idx < 1:\n            return None\n        ve = self._cached_ve[(layer_idx - 1) % self.n_ve]\n        ve_ctx = F.pad(ve[:, :-1, :], (0, 0, 1, 0))\n        return self.ve_alpha[layer_idx] * ve + self.ve_beta[layer_idx] * ve_ctx\n\n    def get_lm_head_weight(self):\n        return self.wte.weight\n\n    def get_num_pos_params(self):\n        return self.wpe.weight.numel()\n\n"
7}
Tool ResultStep 1
OK: Replaced lines 115..140 with 42 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–156, 281–283.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–156 | total: 489 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  class TokenEmbedding(nn.Module):
   116      """VE-LCR: Value Embeddings with Local Context Residual.
   117      Cyclically injects 5 value embedding tables at all layers (except layer 0),
   118      each augmented with a previous-token value residual for local context.
   119      Uses raw nn.Parameter (not nn.Embedding) to bypass _init_weights override."""
   120      def __init__(self, config):
   121          super().__init__()
   122          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   123          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   124          self.drop = nn.Dropout(config.dropout)
   125          self.block_size =
AssistantStep 2edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 281,
5  "end_line": 283,
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 = {}\n"
7}
Tool ResultStep 2
OK: Replaced lines 281..283 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–156, 281–283.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–156 | total: 489 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  class TokenEmbedding(nn.Module):
   116      """VE-LCR: Value Embeddings with Local Context Residual.
   117      Cyclically injects 5 value embedding tables at all layers (except layer 0),
   118      each augmented with a previous-token value residual for local context.
   119      Uses raw nn.Parameter (not nn.Embedding) to bypass _init_weights override."""
   120      def __init__(self, config):
   121          super().__init__()
   122          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   123          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   124          self.drop = nn.Dropout(config.dropout)
   125          self.block_size = c
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=0, train_loss=11.0182, val_loss=10.9786

### lm-eval-345m (scripts/lm_eval_345m.sh)
ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-embedding/claude-opus-4.6_20260408_123010/seed_42/ckpt_gpt-345m.pt
AssistantStep 4edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 115,
5  "end_line": 156,
6  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"Value embeddings with local context residual (VE-LCR).\n    Novel: each value embedding is augmented with a shifted previous-token\n    signal, creating context-dependent value injections at selected layers.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\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        self.n_ve = 5\n        self.vte = nn.Embedding(config.vocab_size * self.n_ve, config.n_embd)\n        nn.init.normal_(self.vte.weight, mean=0.0, std=0.01)\n        self.ve_lambda = nn.Parameter(torch.full((self.n_ve,), 0.5))\n        self.ve_beta = nn.Parameter(torch.full((self.n_ve,), 0.1))\n        self._ve_layers = None\n        self._cached_ve = None\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n        if self._ve_layers is None:\n            self._ve_layers = [1, 2, self.n_layer - 3, self.n_layer - 2, self.n_layer - 1]\n        self._cached_ve = {}\n        for i, layer in enumerate(self._ve_layers):\n            offset = i * self.vocab_size\n            ve = self.vte(idx + offset)\n            ve_shifted = F.pad(ve[:, :-1, :], (0, 0, 1, 0))\n            self._cached_ve[layer] = self.ve_lambda[i] * ve + self.ve_beta[i] * ve_shifted\n        return self.drop(tok_emb + pos_emb)\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_ve is not None and layer_idx in self._cached_ve:\n            return self._cached_ve[layer_idx]\n        return None\n\n    def get_lm_head_weight(self):\n        return self.wte.weight\n\n    def get_num_pos_params(self):\n        return self.wpe.weight.numel()\n\n"
7}
Tool ResultStep 4
OK: Replaced lines 115..156 with 47 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–161, 286–288.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–161 | total: 494 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  class TokenEmbedding(nn.Module):
   116      """Value embeddings with local context residual (VE-LCR).
   117      Novel: each value embedding is augmented with a shifted previous-token
   118      signal, creating context-dependent value injections at selected layers."""
   119      def __init__(self, config):
   120          super().__init__()
   121          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   122          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   123          self.drop = nn.Dropout(config.dropout)
   124          self.block_size = config.block_size
   125          self.n_embd = config.n_embd
   126          self.vocab_s
AssistantStep 5test
AssistantStep 5test
Tool ResultStep 5
[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=8000, train_loss=2.6209, val_loss=2.3408
TRAIN_METRICS: step=9000, train_loss=2.5935, val_loss=2.3205
TRAIN_METRICS: step=10000, train_loss=2.5675, val_loss=2.3022
TRAIN_METRICS: step=11000, train_loss=2.5537, val_loss=2.2836
TRAIN_METRICS: step=12000, train_loss=2.5551, val_loss=2.2763
Final metrics (gpt-345m): val_loss_gpt-345m=2.2762, wikitext2_ppl_gpt-345m=44.1100, lambada_ppl_gpt-345m=70.3600

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.2700, hellaswag_lm-eval-345m=33.6800, piqa_lm-eval-345m=64.5800, winogrande_lm-eval-345m=50.2000

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2762, 'wikitext2_ppl_gpt-345m': 44.11, 'lambada_ppl_gpt-345m': 70.36, 'elapsed_gpt-345m': 20721, 'arc_easy_lm-eval-345m': 56.27, 'hellaswag_lm-eval-345m': 33.68, 'piqa_lm-eval-345m': 64.58, 'wi
AssistantStep 6edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 115,
5  "end_line": 161,
6  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"VE-LCR+: Value Embeddings with Local Context Residual, untied head.\n    Cyclically injects 5 value embedding tables across ALL layers (except 0),\n    each augmented with a shifted previous-token signal for local bigram context.\n    Uses untied lm_head initialized to zero for decoupled output projection.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\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        self.n_ve = 5\n        self.vte = nn.Embedding(config.vocab_size * self.n_ve, config.n_embd)\n        nn.init.normal_(self.vte.weight, mean=0.0, std=0.01)\n        self.ve_lambda = nn.Parameter(torch.full((self.n_ve,), 0.5))\n        self.ve_beta = nn.Parameter(torch.full((self.n_ve,), 0.1))\n        self._cached_ve = None\n        # Untied output head\n        self._lm_head_weight = nn.Parameter(torch.empty(config.vocab_size, config.n_embd))\n        nn.init.zeros_(self._lm_head_weight)\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n        V = self.vocab_size\n        # Pre-compute all 5 VE tables with local context residual\n        self._cached_ve = []\n        for i in range(self.n_ve):\n            ve = self.vte(idx + i * V)\n            ve_shifted = F.pad(ve[:, :-1, :], (0, 0, 1, 0))\n            self._cached_ve.append(self.ve_lambda[i] * ve + self.ve_beta[i] * ve_shifted)\n        return self.drop(tok_emb + pos_emb)\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_ve is None or layer_idx < 1:\n            return None\n        return self._cached_ve[(layer_idx - 1) % self.n_ve]\n\n    def get_lm_head_weight(self):\n        return self._lm_head_weight\n\n    def get_num_pos_params(self):\n        return self.wpe.weight.numel()\n\n"
7}
Tool ResultStep 6
OK: Replaced lines 115..161 with 49 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–163.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–163 | total: 496 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  class TokenEmbedding(nn.Module):
   116      """VE-LCR+: Value Embeddings with Local Context Residual, untied head.
   117      Cyclically injects 5 value embedding tables across ALL layers (except 0),
   118      each augmented with a shifted previous-token signal for local bigram context.
   119      Uses untied lm_head initialized to zero for decoupled output projection."""
   120      def __init__(self, config):
   121          super().__init__()
   122          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   123          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   124          self.drop = nn.Dropout(config.dropout)
   125          self.block
AssistantStep 7test
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=2.6206, val_loss=2.3436
TRAIN_METRICS: step=9000, train_loss=2.5930, val_loss=2.3240
TRAIN_METRICS: step=10000, train_loss=2.5666, val_loss=2.3042
TRAIN_METRICS: step=11000, train_loss=2.5527, val_loss=2.2860
TRAIN_METRICS: step=12000, train_loss=2.5539, val_loss=2.2790
Final metrics (gpt-345m): val_loss_gpt-345m=2.2785, wikitext2_ppl_gpt-345m=44.5600, lambada_ppl_gpt-345m=68.1000

### lm-eval-345m (scripts/lm_eval_345m.sh)
ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-embedding/claude-opus-4.6_20260408_123010/seed_42/ckpt_gpt-345m.pt


[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2785, 'wikitext2_ppl_gpt-345m': 44.56, '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=2.6209, val_loss=2.3408
TRAIN_METRICS: step=9000, train_loss=2.5935, val_loss=2.3205
TRAIN_METRICS: step=10000, train_loss=2.5675, val_loss=2.3022
TRAIN_METRICS: step=11000, train_loss=2.5537, val_loss=2.2836
TRAIN_METRICS: step=12000, train_loss=2.5551, val_loss=2.2763
Final metrics (gpt-345m): val_loss_gpt-345m=2.2762, wikitext2_ppl_gpt-345m=44.1100, lambada_ppl_gpt-345m=70.3600

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.2700, hellaswag_lm-eval-345m=33.6800, piqa_lm-eval-345m=64.5800, winogrande_lm-eval-345m=50.2000

[submit] Finalized test #2 as final.

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2762, 'wikitext2_ppl_gpt-345m': 44.11, 'lambada_ppl_gpt-345m': 70.36, 'elapsed_gpt-345m': 20721, 'arc_easy_lm-eval-345m': 56.27, 'hellaswag_lm-eval-345m': 33.68, 'piqa_lm-eva