Agent Conversation: google/gemini-3.1-pro-preview
Model: google/gemini-3.1-pro-preview
Total steps: 6
Messages: 22
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 2for a hidden secret subsetS - 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
100000steps, 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}.xmust have shape[num_examples, N]with binary values only.ymust have shape[num_examples](or[num_examples, 1]) with binary labels.- Training dataset size must stay
<= 12_800_000examples. get_optimizer_config(...)must returnlr,wd,beta1, andbeta2.
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: hiddAssistantStep 1edit
Tool Input
1{
2 "end_line": 255,
3 "op": "replace",
4 "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n \"\"\"Initialize the fixed two-layer MLP without using the hidden secret.\"\"\"\n for layer in model:\n if isinstance(layer, nn.Linear):\n gain = nn.init.calculate_gain(\"relu\") if layer is model[0] else 1.0\n nn.init.xavier_uniform_(layer.weight, gain=gain)\n nn.init.zeros_(layer.bias)\n\ndef make_dataset(\n secret: tuple[int, ...],\n config: TaskConfig,\n seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Return a dataset with perfectly balanced secret features and random noise for the rest.\"\"\"\n generator = torch.Generator().manual_seed(seed)\n \n K = len(secret)\n num_patterns = 1 << K\n \n # Create 102,400 examples\n num_examples = num_patterns * 400\n \n x = torch.randint(\n low=0,\n high=2,\n size=(num_examples, config.n_features),\n generator=generator,\n dtype=torch.float32,\n )\n \n # Generate all secret patterns\n patterns = torch.arange(num_patterns, dtype=torch.int64).unsqueeze(1)\n patterns = (patterns >> torch.arange(K, dtype=torch.int64)) & 1\n patterns = patterns.to(torch.float32)\n patterns = patterns.repeat(num_examples // num_patterns, 1)\n \n secret_tensor = torch.tensor(secret, dtype=torch.long)\n x[:, secret_tensor] = patterns\n \n y = parity_labels(x, secret)\n return x, y\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n \"\"\"Return AdamW hyperparameters for the fixed training loop.\"\"\"\n return {\n \"lr\": 5e-3,\n \"wd\": 1e-3,\n \"beta1\": 0.9,\n \"beta2\": 0.999,\n }\n",
5 "start_line": 220,
6 "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
7 "after_line": 219
8}Tool ResultStep 1
OK: Replaced lines 220..255 with 50 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–269.
[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–269 | total: 553 lines]
217| # =====================================================================
218| # EDITABLE: init_model, make_dataset, get_optimizer_config
219| # =====================================================================
220 def init_model(model: nn.Sequential, config: TaskConfig) -> None:
221 """Initialize the fixed two-layer MLP without using the hidden secret."""
222 for layer in model:
223 if isinstance(layer, nn.Linear):
224 gain = nn.init.calculate_gain("relu") if layer is model[0] else 1.0
225 nn.init.xavier_uniform_(layer.weight, gain=gain)
226 nn.init.zeros_(layer.bias)
227
228 def make_dataset(
229 secret: tuple[int, ...],
230 con
AssistantStep 2test
AssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "end_line": 257,
4 "start_line": 220,
5 "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
6 "content": "def test_script():\n pass",
7 "after_line": 1
8}Tool ResultStep 1
ERROR: Lines 220..257 exceed the editable range. You may only edit lines 220–255.
[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–255 | total: 539 lines]
217| # =====================================================================
218| # EDITABLE: init_model, make_dataset, get_optimizer_config
219| # =====================================================================
220 def init_model(model: nn.Sequential, config: TaskConfig) -> None:
221 """Initialize the fixed two-layer MLP without using the hidden secret."""
222 for layer in model:
223 if isinstance(layer, nn.Linear):
224 gain = nn.init.calculate_gain("relu") if layer is model[0] else 1.0
225 nn.init.xavier_uniform_(layer.weight, gain=gain)
226 nn.init.zeros_(layer.bias)
227
228
229 def make_dataset(
230 secret: tuple[int, ...],
231 config: TaskConfig,
232 seed: int
AssistantStep 2edit
Tool Input
1{
2 "op": "replace",
3 "start_line": 220,
4 "after_line": 219,
5 "end_line": 255,
6 "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
7 "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n \"\"\"Initialize the fixed two-layer MLP.\"\"\"\n for layer in model:\n if isinstance(layer, nn.Linear):\n gain = nn.init.calculate_gain(\"relu\") if layer is model[0] else 1.0\n nn.init.xavier_uniform_(layer.weight, gain=gain)\n nn.init.zeros_(layer.bias)\n\ndef make_dataset(\n secret: tuple[int, ...],\n config: TaskConfig,\n seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Perfect Cartesian product for secret bits to eliminate gradient noise.\"\"\"\n generator = torch.Generator().manual_seed(seed)\n K = len(secret)\n # 3_840_000 is 30,000 steps * 128 batch_size.\n # 3_840_000 // 256 = 15000.\n num_patterns = 15000 \n \n # Random non-secret features\n x_base = torch.randint(\n low=0,\n high=2,\n size=(num_patterns, 1, config.n_features),\n generator=generator,\n dtype=torch.int64,\n )\n \n # All combinations for secret bits\n # It's an iterator of size 2^K x K\n import itertools\n combs_list = list(itertools.product([0, 1], repeat=K))\n combs = torch.tensor(combs_list, dtype=torch.int64) # [2^K, K]\n \n x = x_base.repeat(1, 1 << K, 1) # [num_patterns, 2^K, N]\n combs = combs.unsqueeze(0).repeat(num_patterns, 1, 1) # [num_patterns, 2^K, K]\n \n for i, sec_idx in enumerate(secret):\n x[:, :, sec_idx] = combs[:, :, i]\n \n x = x.view(-1, config.n_features).to(torch.float32)\n y = parity_labels(x, secret)\n return x, y\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n return {\n \"lr\": 2e-3,\n \"wd\": 1e-1,\n \"beta1\": 0.9,\n \"beta2\": 0.999,\n }"
8}Tool ResultStep 2
OK: Replaced lines 220..255 with 52 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–271.
[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–271 | total: 555 lines]
217| # =====================================================================
218| # EDITABLE: init_model, make_dataset, get_optimizer_config
219| # =====================================================================
220 def init_model(model: nn.Sequential, config: TaskConfig) -> None:
221 """Initialize the fixed two-layer MLP."""
222 for layer in model:
223 if isinstance(layer, nn.Linear):
224 gain = nn.init.calculate_gain("relu") if layer is model[0] else 1.0
225 nn.init.xavier_uniform_(layer.weight, gain=gain)
226 nn.init.zeros_(layer.bias)
227
228 def make_dataset(
229 secret: tuple[int, ...],
230 config: TaskConfig,
231 see
AssistantStep 3test
Tool ResultStep 3
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)
### n32-k8 (scripts/eval.sh)
[COMMAND FAILED exit=1]
TASK_CONFIG N=32 K=8 W=512 num_hidden_secrets=5 num_orderings=3 test_set_size=16384 batch_size=128 max_steps=30000
DATASET_METRICS secret=0 num_examples=3840000 positive_rate=0.500000
Traceback (most recent call last):
File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/optimization-parity/google_gemini-3.1-pro-preview_20260405_233300/pytorch-examples/optimization_parity/custom_strategy.py", line 555, in <module>
main()
File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/optimization-parity/google_gemini-3.1-pro-preview_20260405_233300/pytorch-examples/optimization_parity/custom_strategy.py", line 546, in main
summary = run_benchmark(config=config, seed=args.seed, device=device)
File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/optimization-parity/google_gemini-3.1-pro-preview_20260405_233300/pytorch-examples/optimization_parity/custom_strategy.py"
AssistantStep 1edit
Tool Input
1{
2 "start_line": 220,
3 "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
4 "end_line": 255,
5 "after_line": 219,
6 "op": "replace",
7 "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n \"\"\"Initialize the fixed two-layer MLP without using the hidden secret.\"\"\"\n for layer in model:\n if isinstance(layer, nn.Linear):\n gain = nn.init.calculate_gain(\"relu\") if layer is model[0] else 1.0\n nn.init.xavier_uniform_(layer.weight, gain=gain)\n if layer is model[0]:\n nn.init.normal_(layer.bias, mean=0.0, std=1.0)\n else:\n nn.init.zeros_(layer.bias)\n\n\ndef make_dataset(\n secret: tuple[int, ...],\n config: TaskConfig,\n seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Return a reproducible training dataset for one hidden secret.\"\"\"\n generator = torch.Generator().manual_seed(seed)\n S = len(secret)\n\n # 1. Perfectly balanced uniform subset\n repeats = 1000 # 1000 * 256 = 256,000 examples\n grid = torch.cartesian_prod(*[torch.tensor([0, 1])] * S)\n grid = grid.repeat(repeats, 1)\n \n x_unif = torch.randint(0, 2, size=(repeats * (2**S), config.n_features), generator=generator)\n for idx, sec_idx in enumerate(secret):\n x_unif[:, sec_idx] = grid[:, idx]\n\n # 2. Skewed subset to provide gradient signal (p=0.1 on secret features)\n num_skewed = 256_000\n x_skew = torch.randint(0, 2, size=(num_skewed, config.n_features), generator=generator)\n skewed_vals = (torch.rand(num_skewed, S, generator=generator) < 0.1).int()\n for idx, sec_idx in enumerate(secret):\n x_skew[:, sec_idx] = skewed_vals[:, idx]\n\n x = torch.cat([x_unif, x_skew], dim=0).to(torch.float32)\n y = parity_labels(x, secret)\n return x, y\n\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n \"\"\"Return AdamW hyperparameters for the fixed training loop.\"\"\"\n return {\n \"lr\": 2e-3,\n \"wd\": 1e-2,\n \"beta1\": 0.9,\n \"beta2\": 0.999,\n }"
8}Tool ResultStep 1
OK: Replaced lines 220..255 with 50 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–269.
[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–269 | total: 553 lines]
217| # =====================================================================
218| # EDITABLE: init_model, make_dataset, get_optimizer_config
219| # =====================================================================
220 def init_model(model: nn.Sequential, config: TaskConfig) -> None:
221 """Initialize the fixed two-layer MLP without using the hidden secret."""
222 for layer in model:
223 if isinstance(layer, nn.Linear):
224 gain = nn.init.calculate_gain("relu") if layer is model[0] else 1.0
225 nn.init.xavier_uniform_(layer.weight, gain=gain)
226 if layer is model[0]:
227 nn.init.normal_(layer.bias, mean=0.0, std=1.0)
228 e
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=7000 loss=0.024595 acc=0.998781 TRAIN_METRICS secret=4 order=2 step=7250 loss=0.022150 acc=0.999594 TRAIN_METRICS secret=4 order=2 step=7500 loss=0.019202 acc=0.999750 TRAIN_METRICS secret=4 order=2 step=7750 loss=0.016531 acc=0.999938 TRAIN_METRICS secret=4 order=2 step=8000 loss=0.014315 acc=1.000000 Recent runs: RUN_METRICS secret=3 order=1 steps=9250 test_accuracy=1.000000 RUN_METRICS secret=3 order=2 steps=11250 test_accuracy=1.000000 RUN_METRICS secret=4 order=0 steps=10750 test_accuracy=1.000000 RUN_METRICS secret=4 order=1 steps=9750 test_accuracy=0.998901 RUN_METRICS secret=4 order=2 steps=8000 test_accuracy=1.000000 Final metrics: FINAL_METRICS test_accuracy=0.999878 score=0.999878 test_accuracy_std=0.000297 mean_steps=11916.666667 num_runs=15 ### n50-k8 (scripts/eval_n50_k8.sh) Training progress: TRAIN_METR
AssistantStep 3edit
Tool Input
1{
2 "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
3 "end_line": 269,
4 "after_line": 219,
5 "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n \"\"\"Initialize the fixed two-layer MLP without using the hidden secret.\"\"\"\n for layer in model:\n if isinstance(layer, nn.Linear):\n gain = nn.init.calculate_gain(\"relu\") if layer is model[0] else 1.0\n nn.init.xavier_uniform_(layer.weight, gain=gain)\n if layer is model[0]:\n nn.init.normal_(layer.bias, mean=0.0, std=1.0)\n else:\n nn.init.zeros_(layer.bias)\n\n\ndef make_dataset(\n secret: tuple[int, ...],\n config: TaskConfig,\n seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Return a reproducible training dataset for one hidden secret.\"\"\"\n generator = torch.Generator().manual_seed(seed)\n S = len(secret)\n\n # 1. Perfectly balanced uniform subset\n repeats = 4000 # 4000 * 256 = 1,024,000 examples\n grid = torch.cartesian_prod(*[torch.tensor([0, 1])] * S)\n grid = grid.repeat(repeats, 1)\n \n x_unif = torch.randint(0, 2, size=(repeats * (2**S), config.n_features), generator=generator)\n for idx, sec_idx in enumerate(secret):\n x_unif[:, sec_idx] = grid[:, idx]\n\n # 2. Skewed subset to provide gradient signal (p=0.1 on secret features, 0 elsewhere)\n num_skewed = 1_024_000\n x_skew = torch.zeros((num_skewed, config.n_features), dtype=torch.int64)\n skewed_vals = (torch.rand(num_skewed, S, generator=generator) < 0.1).int()\n for idx, sec_idx in enumerate(secret):\n x_skew[:, sec_idx] = skewed_vals[:, idx]\n\n x = torch.cat([x_unif, x_skew], dim=0).to(torch.float32)\n y = parity_labels(x, secret)\n return x, y\n\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n \"\"\"Return AdamW hyperparameters for the fixed training loop.\"\"\"\n return {\n \"lr\": 4e-3,\n \"wd\": 2e-2,\n \"beta1\": 0.9,\n \"beta2\": 0.999,\n }",
6 "start_line": 220,
7 "op": "replace"
8}Tool ResultStep 3
OK: Replaced lines 220..269 with 50 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–269.
[Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–269 | total: 553 lines]
217| # =====================================================================
218| # EDITABLE: init_model, make_dataset, get_optimizer_config
219| # =====================================================================
220 def init_model(model: nn.Sequential, config: TaskConfig) -> None:
221 """Initialize the fixed two-layer MLP without using the hidden secret."""
222 for layer in model:
223 if isinstance(layer, nn.Linear):
224 gain = nn.init.calculate_gain("relu") if layer is model[0] else 1.0
225 nn.init.xavier_uniform_(layer.weight, gain=gain)
226 if layer is model[0]:
227 nn.init.normal_(layer.bias, mean=0.0, std=1.0)
228 e
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call submit(n=2) to submit this result) ### n32-k8 (scripts/eval.sh) [output file not found: /srv/home/bohanlyu/MLS-Bench/logs/optimization-parity/google_gemini-3.1-pro-preview_20260405_234119/20260406_010738/group_1_0/n32-k8_s42.out] ### n50-k8 (scripts/eval_n50_k8.sh) Training progress: TRAIN_METRICS secret=4 order=2 step=7500 loss=0.024611 acc=0.998375 TRAIN_METRICS secret=4 order=2 step=7750 loss=0.022386 acc=0.999031 TRAIN_METRICS secret=4 order=2 step=8000 loss=0.020291 acc=0.999375 TRAIN_METRICS secret=4 order=2 step=8250 loss=0.019581 acc=0.999500 TRAIN_METRICS secret=4 order=2 step=8500 loss=0.018682 acc=0.999406 Recent runs: RUN_METRICS secret=3 order=1 steps=9000 test_accuracy=1.000000 RUN_METRICS secret=3 order=2 steps=11750 test_accuracy=0.999451 RUN_METRICS secret=4 order=0 steps=11500 test_accuracy=0.999756 RUN_METRICS secret=4 order=1 steps=12000 test_accuracy=0.999939 RUN_METRICS secret=4 order=2 steps=8500 test_accuracy=0.998047 Final
AssistantStep 5edit
Tool Input
1{
2 "filename": "pytorch-examples/optimization_parity/custom_strategy.py",
3 "op": "replace",
4 "after_line": 219,
5 "end_line": 269,
6 "start_line": 220,
7 "content": "def init_model(model: nn.Sequential, config: TaskConfig) -> None:\n \"\"\"Initialize the fixed two-layer MLP without using the hidden secret.\"\"\"\n import math\n for layer in model:\n if isinstance(layer, nn.Linear):\n if layer is model[0]:\n gain = math.sqrt(config.n_features / 32.0) * math.sqrt(2.0)\n nn.init.xavier_uniform_(layer.weight, gain=gain)\n nn.init.normal_(layer.bias, mean=0.0, std=1.0)\n else:\n nn.init.xavier_uniform_(layer.weight, gain=1.0)\n nn.init.zeros_(layer.bias)\n\n\ndef make_dataset(\n secret: tuple[int, ...],\n config: TaskConfig,\n seed: int,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Return a reproducible training dataset for one hidden secret.\"\"\"\n generator = torch.Generator().manual_seed(seed)\n S = len(secret)\n\n # 1. Perfectly balanced uniform subset\n repeats = 4000 # 4000 * 256 = 1,024,000 examples\n grid = torch.cartesian_prod(*[torch.tensor([0, 1])] * S)\n grid = grid.repeat(repeats, 1)\n \n x_unif = torch.randint(0, 2, size=(repeats * (2**S), config.n_features), generator=generator)\n for idx, sec_idx in enumerate(secret):\n x_unif[:, sec_idx] = grid[:, idx]\n\n # 2. Skewed subset to provide gradient signal (p=0.1 on secret features, 0 elsewhere)\n num_skewed = 2_048_000\n x_skew = torch.zeros((num_skewed, config.n_features), dtype=torch.int64)\n skewed_vals = (torch.rand(num_skewed, S, generator=generator) < 0.1).int()\n for idx, sec_idx in enumerate(secret):\n x_skew[:, sec_idx] = skewed_vals[:, idx]\n\n x = torch.cat([x_unif, x_skew], dim=0).to(torch.float32)\n y = parity_labels(x, secret)\n return x, y\n\n\ndef get_optimizer_config(config: TaskConfig) -> dict[str, float]:\n \"\"\"Return AdamW hyperparameters for the fixed training loop.\"\"\"\n return {\n \"lr\": 5e-3,\n \"wd\": 2e-2,\n \"beta1\": 0.9,\n \"beta2\": 0.999,\n }"
8}Tool ResultStep 5
OK: Replaced lines 220..269 with 52 line(s) in pytorch-examples/optimization_parity/custom_strategy.py. Editable range: 220–271. [Current file: pytorch-examples/optimization_parity/custom_strategy.py | editable: 220–271 | total: 555 lines] 217| # ===================================================================== 218| # EDITABLE: init_model, make_dataset, get_optimizer_config 219| # ===================================================================== 220 def init_model(model: nn.Sequential, config: TaskConfig) -> None: 221 """Initialize the fixed two-layer MLP without using the hidden secret.""" 222 import math 223 for layer in model: 224 if isinstance(layer, nn.Linear): 225 if layer is model[0]: 226 gain = math.sqrt(config.n_features / 32.0) * math.sqrt(2.0) 227 nn.init.xavier_uniform_(layer.weight, gain=gain) 228 nn.init.normal_(layer.bias, mean=0.0, std=1.