Agent Conversation: openai/gpt-5.4-pro

Model: openai/gpt-5.4-pro

Total steps: 5

Messages: 11

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  "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n    \"\"\"Secret-agnostic hinge bank that only updates on coordinates seen in data.\"\"\"\n    first, second = model[0], model[2]\n    num_knots = config.secret_size + 1\n    unit_ids = torch.arange(config.hidden_width)\n    knot_ids = unit_ids % num_knots\n    replica_ids = unit_ids // num_knots\n    max_replica = max((config.hidden_width - 1) // num_knots, 1)\n    with torch.no_grad():\n        first.weight.zero_()\n        first.bias.copy_(\n            0.03\n            + 0.20 * (knot_ids.to(torch.float32) + 0.5) / num_knots\n            + 0.01 * replica_ids.to(torch.float32) / max_replica\n        )\n        second.weight.zero_()\n        signs = torch.where((knot_ids % 2) == 0, 1.0, -1.0).to(torch.float32)\n        scales = 0.10 + 0.02 * ((replica_ids % 3).to(torch.float32) - 1.0)\n        second.weight[0].copy_(signs * scales)\n        second.bias.zero_()\n        second.bias[0] = -torch.dot(second.weight[0], first.bias)\n\n\ndef make_dataset(\n    secret: tuple[int, ...],\n    config: TaskConfig,\n    seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Mixture of uniform and sparse-biased secret-only patterns.\"\"\"\n    generator = torch.Generator().manual_seed(seed)\n    k = config.secret_size\n    patterns = torch.arange(1 << k, dtype=torch.long)\n    secret_bits = ((patterns[:, None] >> torch.arange(k, dtype=torch.long)) & 1).to(torch.float32)\n    counts = secret_bits.sum(dim=1)\n    mix = 0.75\n    p = 1.0 / max(k, 2)\n    target_examples = 16_384\n    probs = mix * (p**counts) * ((1.0 - p) ** (k - counts)) + (1.0 - mix) / float(1 << k)\n    raw_repeats = target_examples * probs\n    repeats = raw_repeats.floor().to(torch.long)\n    leftover = int(target_examples - repeats.sum().item())\n    if leftover > 0:\n        extra = torch.multinomial(\n            raw_repeats - repeats.to(torch.float32),\n            leftover,\n            replacement=False,\n            generator=generator,\n        )\n        repeats[extra] += 1\n    x_secret = secret_bits.repeat_interleave(repeats, dim=0)\n    y = x_secret.sum(dim=1).remainder(2)\n    x = torch.zeros((x_secret.shape[0], config.n_features), dtype=torch.float32)\n    x[:, torch.tensor(secret, dtype=torch.long)] = x_secret\n    perm = torch.randperm(x.shape[0], generator=generator)\n    return x[perm], y[perm]\n\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n    \"\"\"AdamW tuned for the low-dimensional secret-only curriculum.\"\"\"\n    return {\n        \"lr\": 3e-3,\n        \"wd\": 0.0,\n        \"beta1\": 0.9,\n        \"beta2\": 0.99,\n    }\n",
5  "after_line": 0,
6  "start_line": 220,
7  "end_line": 255
8}

Tool ResultStep 1

OK: Replaced lines 220..255 with 65 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–284.

[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–284 | total: 568 lines]
   217| # =====================================================================
   218| # EDITABLE: init_model, make_dataset, get_optimizer_config
   219| # =====================================================================
   220  def init_model(model: nn.Sequential, config: TaskConfig) -> None:
   221      """Secret-agnostic hinge bank that only updates on coordinates seen in data."""
   222      first, second = model[0], model[2]
   223      num_knots = config.secret_size + 1
   224      unit_ids = torch.arange(config.hidden_width)
   225      knot_ids = unit_ids % num_knots
   226      replica_ids = unit_ids // num_knots
   227      max_replica = max((config.hidden_width - 1) // num_knots, 1)
   228      with torch.no_grad():
   229

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=29000 loss=0.017746 acc=0.992812
TRAIN_METRICS secret=4 order=2 step=29250 loss=0.019536 acc=0.991781
TRAIN_METRICS secret=4 order=2 step=29500 loss=0.018767 acc=0.992281
TRAIN_METRICS secret=4 order=2 step=29750 loss=0.018911 acc=0.992219
TRAIN_METRICS secret=4 order=2 step=30000 loss=0.019773 acc=0.991594

Recent runs:
RUN_METRICS secret=3 order=1 steps=30000 test_accuracy=0.969360
RUN_METRICS secret=3 order=2 steps=30000 test_accuracy=0.969360
RUN_METRICS secret=4 order=0 steps=30000 test_accuracy=0.965759
RUN_METRICS secret=4 order=1 steps=30000 test_accuracy=0.965759
RUN_METRICS secret=4 order=2 steps=30000 test_accuracy=0.965759

Final metrics:
FINAL_METRICS test_accuracy=0.968359 score=0.968359 test_accuracy_std=0.001330 mean_steps=30000.000000 num_runs=15

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

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
4  "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n    \"\"\"Secret-agnostic hinge bank that only updates on coordinates seen in data.\"\"\"\n    first, second = model[0], model[2]\n    num_knots = config.secret_size + 1\n    unit_ids = torch.arange(config.hidden_width)\n    knot_ids = unit_ids % num_knots\n    replica_ids = unit_ids // num_knots\n    max_replica = max((config.hidden_width - 1) // num_knots, 1)\n    with torch.no_grad():\n        first.weight.zero_()\n        first.bias.copy_(\n            0.03\n            + 0.20 * (knot_ids.to(torch.float32) + 0.5) / num_knots\n            + 0.01 * replica_ids.to(torch.float32) / max_replica\n        )\n        second.weight.zero_()\n        signs = torch.where((knot_ids % 2) == 0, 1.0, -1.0).to(torch.float32)\n        scales = 0.10 + 0.02 * ((replica_ids % 3).to(torch.float32) - 1.0)\n        second.weight[0].copy_(signs * scales)\n        second.bias.zero_()\n        second.bias[0] = -torch.dot(second.weight[0], first.bias)\n\n\ndef make_dataset(\n    secret: tuple[int, ...],\n    config: TaskConfig,\n    seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Count-balanced secret-only shells: strong signal plus full parity coverage.\"\"\"\n    generator = torch.Generator().manual_seed(seed)\n    k = config.secret_size\n    patterns = torch.arange(1 << k, dtype=torch.long)\n    secret_bits = ((patterns[:, None] >> torch.arange(k, dtype=torch.long)) & 1).to(torch.float32)\n    counts = secret_bits.sum(dim=1).to(torch.long)\n    shell_examples = 2_048\n    repeats = torch.zeros_like(counts)\n    for r in range(k + 1):\n        idx = torch.nonzero(counts == r, as_tuple=False).view(-1)\n        base = shell_examples // idx.numel()\n        repeats[idx] = base\n        extra = shell_examples - base * idx.numel()\n        if extra > 0:\n            chosen = idx[torch.randperm(idx.numel(), generator=generator)[:extra]]\n            repeats[chosen] += 1\n    x_secret = secret_bits.repeat_interleave(repeats, dim=0)\n    y = x_secret.sum(dim=1).remainder(2)\n    x = torch.zeros((x_secret.shape[0], config.n_features), dtype=torch.float32)\n    x[:, torch.tensor(secret, dtype=torch.long)] = x_secret\n    perm = torch.randperm(x.shape[0], generator=generator)\n    return x[perm], y[perm]\n\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n    \"\"\"AdamW for the count-balanced secret-shell curriculum.\"\"\"\n    return {\n        \"lr\": 4e-3,\n        \"wd\": 0.0,\n        \"beta1\": 0.9,\n        \"beta2\": 0.98,\n    }\n",
5  "after_line": 0,
6  "start_line": 220,
7  "end_line": 284
8}

Tool ResultStep 3

OK: Replaced lines 220..284 with 60 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–279.

[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–279 | total: 563 lines]
   217| # =====================================================================
   218| # EDITABLE: init_model, make_dataset, get_optimizer_config
   219| # =====================================================================
   220  def init_model(model: nn.Sequential, config: TaskConfig) -> None:
   221      """Secret-agnostic hinge bank that only updates on coordinates seen in data."""
   222      first, second = model[0], model[2]
   223      num_knots = config.secret_size + 1
   224      unit_ids = torch.arange(config.hidden_width)
   225      knot_ids = unit_ids % num_knots
   226      replica_ids = unit_ids // num_knots
   227      max_replica = max((config.hidden_width - 1) // num_knots, 1)
   228      with torch.no_grad():
   229

AssistantStep 4test

Tool ResultStep 4

[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=2500 loss=0.080808 acc=0.994969
TRAIN_METRICS secret=4 order=2 step=2750 loss=0.033941 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=3000 loss=0.011566 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=3250 loss=0.003456 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=3500 loss=0.000935 acc=1.000000

Recent runs:
RUN_METRICS secret=3 order=1 steps=4500 test_accuracy=1.000000
RUN_METRICS secret=3 order=2 steps=3750 test_accuracy=1.000000
RUN_METRICS secret=4 order=0 steps=3500 test_accuracy=1.000000
RUN_METRICS secret=4 order=1 steps=4000 test_accuracy=1.000000
RUN_METRICS secret=4 order=2 steps=3500 test_accuracy=1.000000

Final metrics:
FINAL_METRICS test_accuracy=1.000000 score=1.000000 test_accuracy_std=0.000000 mean_steps=3850.000000 num_runs=15

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

AssistantStep 5submit

Tool Input

1{
2  "n": 2
3}

Tool ResultStep 5

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

### n32-k8 (scripts/eval.sh)
Training progress:
TRAIN_METRICS secret=4 order=2 step=2500 loss=0.080808 acc=0.994969
TRAIN_METRICS secret=4 order=2 step=2750 loss=0.033941 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=3000 loss=0.011566 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=3250 loss=0.003456 acc=1.000000
TRAIN_METRICS secret=4 order=2 step=3500 loss=0.000935 acc=1.000000

Recent runs:
RUN_METRICS secret=3 order=1 steps=4500 test_accuracy=1.000000
RUN_METRICS secret=3 order=2 steps=3750 test_accuracy=1.000000
RUN_METRICS secret=4 order=0 steps=3500 test_accuracy=1.000000
RUN_METRICS secret=4 order=1 steps=4000 test_accuracy=1.000000
RUN_METRICS secret=4 order=2 steps=3500 test_accuracy=1.000000

Final metrics:
FINAL_METRICS test_accuracy=1.000000 score=1.000000 test_accuracy_std=0.000000 mean_steps=3850.000000 num_runs=15

### n50-k8 (scripts/eval_n50_k8.sh)
Training progress:
TRAIN_METRICS secret=4 order=2 step=2250 loss=