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YangyiH/qwen3-4b-teacher-rollouts-76k-nonthinking

Qwen3-4B Teacher Rollouts 76K Non-Thinking This dataset contains 76,800 fixed teacher trajectories generated for a prompt-aligned reproduction study of on-policy distillation with Qwen3-1.7B. It is an independent research artifact, not an official release from the model or paper authors. Models and generation Teacher: Qwen/Qwen3-4B-Instruct-2507 Tokenizer/chat template: Qwen/Qwen3-1.7B Mode: non-thinking (enable_thinking=False) Temperature: 0.7 Top-p: 1.0 Top-k:… See the full description on the dataset page: https://huggingface.co/datasets/YangyiH/qwen3-4b-teacher-rollouts-76k-nonthinking.

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Qwen3-4B Teacher Rollouts 76K Non-Thinking

This dataset contains 76,800 fixed teacher trajectories generated for a prompt-aligned reproduction study of on-policy distillation with Qwen3-1.7B. It is an independent research artifact, not an official release from the model or paper authors.

Models and generation

  • —Teacher: Qwen/Qwen3-4B-Instruct-2507
  • —Tokenizer/chat template: Qwen/Qwen3-1.7B
  • —Mode: non-thinking (enable_thinking=False)
  • —Temperature: 0.7
  • —Top-p: 1.0
  • —Top-k: 0
  • —Maximum prompt length: 4,096 tokens
  • —Maximum completion length: 8,192 tokens
  • —Seed for row i: 42 + i

Prompts were passed to the vLLM completions API as exact token IDs with no additional special tokens. Returned token IDs and special tokens are retained.

Statistics

StatisticValue
Rows76,800
Prompt tokens21,596,089
Completion tokens264,338,320
Total stored tokens285,934,409
Mean completion length3,441.91
Completions reaching 8,192 tokens10,974 (14.29%)

The first 25,600 trajectories exactly reuse the earlier 100-step teacher bank. The remaining 51,200 rows extend the same seed-42 prompt permutation without an epoch wrap.

Fields

  • —input_ids: concatenated prompt and completion token IDs
  • —labels: -100 over prompt tokens and completion token IDs thereafter
  • —prompt_ids, completion_ids: separate exact token sequences
  • —messages: original conversation plus decoded teacher assistant response
  • —domain: math, science, or code
  • —source_dataset, source_config, source_split, source_shard, source_id: upstream provenance
  • —prompt_sha256: hash of the source prompt
  • —source_row_index, unified_order_index: source and rollout schedule indices
  • —finish_reason, generation_seed: generation metadata

Integrity

  • —Prompt order SHA-256: c1cc096fa94f82bf44457673b112039f604aaba19711a850f368514da6d7429d
  • —Completion IDs SHA-256: a3c5ae8551b23025bb57d6d60a011ef7e156d034d0853656dbb33d29b064127b

The repository includes the full generation manifest. A post-generation audit found zero prompt-identity mismatches, and the prompt-token hash matches a fresh application of the documented Qwen non-thinking chat template.

Usage

python
from datasets import load_dataset

dataset = load_dataset(
    "YangyiH/qwen3-4b-teacher-rollouts-76k-nonthinking",
    split="train",
)

Source data and licensing

Prompts originate from the NVIDIA OpenMathReasoning, OpenScienceReasoning-2, and OpenCodeReasoning datasets. This repository does not assert a new blanket license over upstream prompt content. Users should review and comply with the source datasets' licenses, terms, and attribution requirements, as well as the Qwen model license.

Limitations

  • —The source mixture intentionally retains duplicate prompts inherited from the upstream datasets.
  • —No additional quality, difficulty, benchmark-contamination, or safety filtering was applied.
  • —14.29% of completions reached the configured 8,192-token cap and may be truncated rather than naturally terminated.
  • —Generated responses can contain errors or undesirable content inherited from the model and source prompts.