| --- |
| pretty_name: Qwen3-4B Teacher Rollouts 76K Non-Thinking |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - reasoning |
| - knowledge-distillation |
| - teacher-rollout |
| - qwen3 |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| # 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 |
|
|
| | Statistic | Value | |
| | --- | ---: | |
| | Rows | 76,800 | |
| | Prompt tokens | 21,596,089 | |
| | Completion tokens | 264,338,320 | |
| | Total stored tokens | 285,934,409 | |
| | Mean completion length | 3,441.91 | |
| | Completions reaching 8,192 tokens | 10,974 (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. |
|
|
