wmc-sft-baseline-v2

SFT warmup dataset for baseline GRPO condition. 1200 Countdown problems (5 numbers, +/-/*) solved by Qwen3-1.7B with 32k token generation. Contains full untruncated reasoning traces. No confidence tags.

Dataset Info

• Rows: 1200
• Columns: 3

Columns

Column	Type	Description
messages	List({'content': Value('string'), 'role': Value('string')})	Chat-format conversation (system, user, assistant). System sets task format, user provides numbers/target, assistant contains full reasoning trace in tags followed by answer.
correct	Value('bool')	Boolean indicating whether the model's final expression evaluates to the target number.
difficulty	Value('string')	Problem difficulty tier: easy, medium, or hard (based on number count and operator complexity).

Generation Parameters

{
  "script_name": "generate_sft_data.py",
  "model": "Qwen/Qwen3-1.7B",
  "description": "SFT warmup dataset for baseline GRPO condition. 1200 Countdown problems (5 numbers, +/-/*) solved by Qwen3-1.7B with 32k token generation. Contains full untruncated reasoning traces. No confidence tags.",
  "hyperparameters": {
    "temperature": 0.7,
    "max_tokens": 32768,
    "top_p": 0.9
  },
  "input_datasets": []
}

Experiment Documentation

For complete experiment details, see https://github.com/Zayne-sprague/SC-Research-Notes/tree/main/experiments/world-model-curiosity

Usage

from datasets import load_dataset

dataset = load_dataset("reasoning-degeneration-dev/wmc-sft-baseline-v2", split="train")
print(f"Loaded {len(dataset)} rows")

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This dataset is tracked in reasoning-degeneration-dev/PROJECT-MANIFEST