Agent Conversation: anthropic/claude-opus-4.6

Model: anthropic/claude-opus-4.6

Total steps: 7

Messages: 19

UserStep 0

Task: optimization-parity

Optimization Parity

Research Question

Can you improve a fixed two-layer MLP's ability to learn sparse parity by designing only its initialization, training dataset, and AdamW hyperparameters?

What You Can Modify

Edit the scaffold file pytorch-examples/optimization_parity/custom_strategy.py only inside the editable block containing:

init_model(model, config)
make_dataset(secret, config, seed)
get_optimizer_config(config)

The benchmark is evaluated on three configurations: (N=32, K=8), (N=50, K=8), and (N=64, K=8), all with W=512.

Fixed Setup

Task: y = (sum_{i in S} x_i) mod 2 for a hidden secret subset S
Inputs: binary vectors x in {0,1}^N
Model: Linear(N, W) -> ReLU -> Linear(W, 1) -> Sigmoid
Optimizer type: AdamW
Loss: binary cross-entropy
Batch size: 128
Training budget: up to 100000 steps, reshuffling every epoch
Evaluation: 10 hidden secrets, 10 random epoch-orderings per secret, mean held-out test accuracy over all 100 runs

Interface Notes

init_model(...) must not depend on the hidden secret.
make_dataset(...) may use the provided secret and must return either (x, y) or {"x": x, "y": y}.
x must have shape [num_examples, N] with binary values only.
y must have shape [num_examples] (or [num_examples, 1]) with binary labels.
Training dataset size must stay <= 12_800_000 examples.
get_optimizer_config(...) must return lr, wd, beta1, and beta2.

Metric

The leaderboard metric is test_accuracy (also emitted as score), the mean test accuracy across all 100 training runs.

Hints

The dataset generator knows the sampled secret even though the agent writing the code does not.
Balanced or structured coverage of the secret coordinates can matter more than raw dataset size.
The benchmark driver already handles training, evaluation, reproducibility, and logging.

pytorch-examples/optimization_parity/custom_strategy.py [EDITABLE — lines 220–255 only]

     1: """Optimization-parity scaffold for MLS-Bench.
     2: 
     3: The fixed evaluation samples hidden sparse parity functions and asks the agent
     4: to control only:
     5:   1. model initialization
     6:   2. training-data generation
     7:   3. AdamW hyperparameters
     8: """
     9: 
    10: from __future__ import annotations
    11: 
    12: import argparse
    13: import json
    14: import math
    15: import random
    16: from dataclasses import asdict, dataclass, replace
    17: from pathlib import Path
    18: 
    19: import torch
    20: from torch import nn
    21: 
    22: 
    23: # =====================================================================
    24: # FIXED: Benchmark configuration
    25: # =====================================================================
    26: @dataclass(frozen=True)
    27: class TaskConfig:
    28:     n_features: int = 32
    29:     secret_size: int = 8
    30:     hidd

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
4  "start_line": 220,
5  "end_line": 255,
6  "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n    \"\"\"Scaled init: larger first layer for diverse features, tiny output for max sigmoid gradient.\"\"\"\n    layer0 = model[0]\n    layer2 = model[2]\n    # Larger first-layer scale helps break symmetry faster for parity\n    nn.init.kaiming_normal_(layer0.weight, mode='fan_in', nonlinearity='relu')\n    layer0.weight.data *= 2.0\n    nn.init.zeros_(layer0.bias)\n    # Small output keeps sigmoid(output) ≈ 0.5, maximizing gradient magnitude\n    nn.init.normal_(layer2.weight, mean=0.0, std=0.01)\n    nn.init.zeros_(layer2.bias)\n\n\ndef make_dataset(\n    secret: tuple[int, ...],\n    config: TaskConfig,\n    seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Exhaustive Parity Coverage with Feature Isolation (EPCFI).\n\n    Enumerates all 2^K secret-bit patterns for perfect truth-table coverage,\n    paired with clean copies (zero non-secret bits for noise-free gradient)\n    and noisy copies (random non-secret bits for feature invariance learning).\n    \"\"\"\n    generator = torch.Generator().manual_seed(seed)\n    K = len(secret)\n    N = config.n_features\n\n    num_patterns = 1 << K  # 256 for K=8\n    # Build exhaustive secret-bit truth table via bit manipulation\n    arange = torch.arange(num_patterns, dtype=torch.long)\n    secret_bits = torch.zeros(num_patterns, K, dtype=torch.float32)\n    for j in range(K):\n        secret_bits[:, j] = ((arange >> j) & 1).float()\n\n    secret_idx = torch.tensor(list(secret), dtype=torch.long)\n    non_secret = sorted(set(range(N)) - set(secret))\n    non_secret_idx = torch.tensor(non_secret, dtype=torch.long)\n    num_non_secret = len(non_secret)\n\n    # Adaptive ratio: more clean data when non-secret dimension is larger\n    reps_total = 40\n    reps_clean = 20\n    reps_noisy = reps_total - reps_clean\n\n    # Clean copies: non-secret bits = 0, pure parity signal\n    n_clean = num_patterns * reps_clean\n    x_clean = torch.zeros(n_clean, N, dtype=torch.float32)\n    x_clean.index_copy_(1, secret_idx,\n                         secret_bits.repeat_interleave(reps_clean, dim=0))\n\n    # Noisy copies: random non-secret bits → learn feature invariance\n    n_noisy = num_patterns * reps_noisy\n    x_noisy = torch.zeros(n_noisy, N, dtype=torch.float32)\n    x_noisy.index_copy_(1, secret_idx,\n                         secret_bits.repeat_interleave(reps_noisy, dim=0))\n    rand_bits = torch.randint(0, 2, (n_noisy, num_non_secret),\n                               generator=generator, dtype=torch.float32)\n    x_noisy.index_copy_(1, non_secret_idx, rand_bits)\n\n    x = torch.cat([x_clean, x_noisy], dim=0)\n    y = parity_labels(x, secret)\n    return x, y\n\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n    \"\"\"Aggressive LR for parity, mild WD to prune irrelevant features.\"\"\"\n    return {\n        \"lr\": 0.01,\n        \"wd\": 1e-3,\n        \"beta1\": 0.9,\n        \"beta2\": 0.98,\n    }\n"
7}

Tool ResultStep 1

OK: Replaced lines 220..255 with 73 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–292.

[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–292 | total: 576 lines]
   217| # =====================================================================
   218| # EDITABLE: init_model, make_dataset, get_optimizer_config
   219| # =====================================================================
   220  def init_model(model: nn.Sequential, config: TaskConfig) -> None:
   221      """Scaled init: larger first layer for diverse features, tiny output for max sigmoid gradient."""
   222      layer0 = model[0]
   223      layer2 = model[2]
   224      # Larger first-layer scale helps break symmetry faster for parity
   225      nn.init.kaiming_normal_(layer0.weight, mode='fan_in', nonlinearity='relu')
   226      layer0.weight.data *= 2.0
   227      nn.init.zeros_(layer0.bias)
   228      # Small output keeps sigmoid(outp

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)


## Seed 42

### n32-k8 (scripts/eval.sh)
[output file not found: /srv/home/bohanlyu/MLS-Bench/logs/optimization-parity/anthropic_claude-opus-4.6_20260405_234121/20260405_234818/group_1_0/n32-k8_s42.out]

### n50-k8 (scripts/eval_n50_k8.sh)
Training progress:
TRAIN_METRICS secret=4 order=2 step=14000 loss=0.003578 acc=0.998875
TRAIN_METRICS secret=4 order=2 step=14250 loss=0.002999 acc=0.999062
TRAIN_METRICS secret=4 order=2 step=14500 loss=0.000046 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=14750 loss=0.000001 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=15000 loss=0.000000 acc=1.000000

Recent runs:
RUN_METRICS secret=3 order=1 steps=21750 test_accuracy=0.503296
RUN_METRICS secret=3 order=2 steps=17000 test_accuracy=0.492310
RUN_METRICS secret=4 order=0 steps=19500 test_accuracy=0.498779
RUN_METRICS secret=4 order=1 steps=19750 test_accuracy=0.493225
RUN_METRICS secret=4 order=2 steps=15000 test_accuracy

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
4  "start_line": 220,
5  "end_line": 255,
6  "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n    \"\"\"Feature-learning init scaled for K-sparse binary inputs.\n    Scale factor sqrt(N/K) compensates for only K active features.\"\"\"\n    sf = math.sqrt(config.n_features / config.secret_size)\n    nn.init.kaiming_normal_(model[0].weight, a=0, mode='fan_in', nonlinearity='relu')\n    model[0].weight.data *= sf\n    nn.init.zeros_(model[0].bias)\n    nn.init.normal_(model[2].weight, std=1.0 / math.sqrt(config.hidden_width))\n    nn.init.zeros_(model[2].bias)\n\n\ndef make_dataset(\n    secret: tuple[int, ...],\n    config: TaskConfig,\n    seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Feature Isolation Training: zero non-secret features, enumerate all secret patterns.\n    Non-secret feature weights receive zero gradient and decay to zero via weight decay,\n    reducing the N-dimensional sparse parity to a K-dimensional problem.\"\"\"\n    K, N = len(secret), config.n_features\n    num_patterns = 2 ** K\n    repeats = max(4, 4096 // num_patterns)\n    total = num_patterns * repeats\n    x = torch.zeros(total, N, dtype=torch.float32)\n    secret_list = list(secret)\n    for i in range(num_patterns):\n        bits = torch.tensor([(i >> j) & 1 for j in range(K)], dtype=torch.float32)\n        x[i * repeats:(i + 1) * repeats, secret_list] = bits.unsqueeze(0)\n    y = parity_labels(x, secret)\n    return x, y\n\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n    \"\"\"AdamW with strong weight decay for irrelevant feature suppression.\"\"\"\n    return {\"lr\": 0.005, \"wd\": 1.0, \"beta1\": 0.9, \"beta2\": 0.98}\n"
7}

Tool ResultStep 1

OK: Replaced lines 220..255 with 35 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–254.

[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–254 | total: 538 lines]
   217| # =====================================================================
   218| # EDITABLE: init_model, make_dataset, get_optimizer_config
   219| # =====================================================================
   220  def init_model(model: nn.Sequential, config: TaskConfig) -> None:
   221      """Feature-learning init scaled for K-sparse binary inputs.
   222      Scale factor sqrt(N/K) compensates for only K active features."""
   223      sf = math.sqrt(config.n_features / config.secret_size)
   224      nn.init.kaiming_normal_(model[0].weight, a=0, mode='fan_in', nonlinearity='relu')
   225      model[0].weight.data *= sf
   226      nn.init.zeros_(model[0].bias)
   227      nn.init.normal_(model[2].weight, std=1.0 / math.sqrt(c

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)


## Seed 42

### n32-k8 (scripts/eval.sh)
Training progress:
TRAIN_METRICS secret=4 order=2 step=750 loss=0.368543 acc=0.998844
TRAIN_METRICS secret=4 order=2 step=1000 loss=0.318710 acc=0.999812
TRAIN_METRICS secret=4 order=2 step=1250 loss=0.309547 acc=0.999656
TRAIN_METRICS secret=4 order=2 step=1500 loss=0.301176 acc=0.999875
TRAIN_METRICS secret=4 order=2 step=1750 loss=0.295127 acc=0.999594

Recent runs:
RUN_METRICS secret=3 order=1 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=3 order=2 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=0 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=1 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=2 steps=1750 test_accuracy=1.000000

Final metrics:
FINAL_METRICS test_accuracy=0.999162 score=0.999162 test_accuracy_std=0.003136 mean_steps=1850.000000 num_runs=15

### n50-k8 (scripts/eval_n50_k8.sh)
Training progress:
TRAIN_METRICS

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
4  "start_line": 252,
5  "end_line": 254,
6  "content": "    \"\"\"AdamW: higher lr for faster confident learning, moderate wd still decays non-secret weights.\"\"\"\n    return {\"lr\": 0.01, \"wd\": 0.3, \"beta1\": 0.9, \"beta2\": 0.95}\n"
7}

Tool ResultStep 3

OK: Replaced lines 252..254 with 2 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–253.

[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–253 | total: 537 lines]
   217| # =====================================================================
   218| # EDITABLE: init_model, make_dataset, get_optimizer_config
   219| # =====================================================================
   220  def init_model(model: nn.Sequential, config: TaskConfig) -> None:
   221      """Feature-learning init scaled for K-sparse binary inputs.
   222      Scale factor sqrt(N/K) compensates for only K active features."""
   223      sf = math.sqrt(config.n_features / config.secret_size)
   224      nn.init.kaiming_normal_(model[0].weight, a=0, mode='fan_in', nonlinearity='relu')
   225      model[0].weight.data *= sf
   226      nn.init.zeros_(model[0].bias)
   227      nn.init.normal_(model[2].weight, std=1.0 / math.sqrt(co

AssistantStep 4undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 4

Undo complete:
Restored: pytorch-examples/optimization_parity/custom_strategy.py

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
4  "start_line": 252,
5  "end_line": 254,
6  "content": "def get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n    \"\"\"AdamW: higher lr for confident learning, moderate wd still decays non-secret weights.\"\"\"\n    return {\"lr\": 0.01, \"wd\": 0.3, \"beta1\": 0.9, \"beta2\": 0.95}\n"
7}

Tool ResultStep 5

OK: Replaced lines 252..254 with 3 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–254.

[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–254 | total: 538 lines]
   217| # =====================================================================
   218| # EDITABLE: init_model, make_dataset, get_optimizer_config
   219| # =====================================================================
   220  def init_model(model: nn.Sequential, config: TaskConfig) -> None:
   221      """Feature-learning init scaled for K-sparse binary inputs.
   222      Scale factor sqrt(N/K) compensates for only K active features."""
   223      sf = math.sqrt(config.n_features / config.secret_size)
   224      nn.init.kaiming_normal_(model[0].weight, a=0, mode='fan_in', nonlinearity='relu')
   225      model[0].weight.data *= sf
   226      nn.init.zeros_(model[0].bias)
   227      nn.init.normal_(model[2].weight, std=1.0 / math.sqrt(co

AssistantStep 6test

Tool ResultStep 6

[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### n32-k8 (scripts/eval.sh)
Training progress:
TRAIN_METRICS secret=4 order=2 step=750 loss=0.035848 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=1000 loss=0.025750 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=1250 loss=0.021509 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=1500 loss=0.017096 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=1750 loss=0.016480 acc=1.000000

Recent runs:
RUN_METRICS secret=3 order=1 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=3 order=2 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=0 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=1 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=2 steps=1750 test_accuracy=1.000000

Final metrics:
FINAL_METRICS test_accuracy=1.000000 score=1.000000 test_accuracy_std=0.000000 mean_steps=1750.000000 num_runs=15

### n50-k8 (scripts/eval_n50_k8.sh)
Training progress:
TRAIN_METRICS secret=4 order

AssistantStep 7submit

Tool Input

1{
2  "n": 2
3}

Tool ResultStep 7

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

### n32-k8 (scripts/eval.sh)
Training progress:
TRAIN_METRICS secret=4 order=2 step=750 loss=0.035848 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=1000 loss=0.025750 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=1250 loss=0.021509 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=1500 loss=0.017096 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=1750 loss=0.016480 acc=1.000000

Recent runs:
RUN_METRICS secret=3 order=1 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=3 order=2 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=0 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=1 steps=1750 test_accuracy=1.000000
RUN_METRICS secret=4 order=2 steps=1750 test_accuracy=1.000000

Final metrics:
FINAL_METRICS test_accuracy=1.000000 score=1.000000 test_accuracy_std=0.000000 mean_steps=1750.000000 num_runs=15

### n50-k8 (scripts/eval_n50_k8.sh)
Training progress:
TRAIN_METRICS secret=4 order=2 step=750 loss=0.