xtools-at/trace-inversion-reasoning-generation-qwen3.8-max

🧠 Qwen3.8-Max Reasoning Generation for Trace Inversion

💡 Overview

This dataset is designed to train language models to generate reasoning traces from only a problem and its known final answer, a process referred to as trace inversion.

Unlike conventional reasoning distillation, which directly trains on teacher-generated reasoning traces, this approach removes intermediate reasoning information from the input. Given only the original question and final answer, the model learns to construct a coherent, step-by-step reasoning trace that plausibly connects the two.

The dataset is derived from saidutta69/qwen3.8-max-distillation-50k-clean, a cleaned collection of reasoning traces generated by Qwen3.8-Max (qwen3.8-max-preview). The original Qwen3.8-Max data provides the underlying problems and answers for a new trace-generation task, producing synthetic reasoning supervision without requiring reasoning bubbles at inference time.

The resulting dataset was sampled to create 10,000 training examples and 500 evaluation examples, with generated reasoning traces limited to 1,200 characters.

📚 Data source

The dataset is based on saidutta69/qwen3.8-max-distillation-50k-clean, a curated dataset containing 49,661 teacher-generated examples from Qwen3.8-Max.

The trace-inversion process uses the source questions and final answers to produce a new reasoning-generation task. Unlike the original Qwen3.8-Max dataset, the model input contains no original reasoning trace, compressed reasoning summary, or reasoning bubbles. The target is a concise, synthetic reasoning trace generated under these constraints.

🧩 Dataset structure

The dataset uses ChatML-style conversations serialized as JSONL, with one example per line. Each record contains a messages array with three roles:

• system — Defines the trace-inversion task and instructs the model to generate reasoning using only the question and final answer.
• user — Contains the original question, the known final answer, and an instruction to generate a reasoning trace.
• assistant — Contains the target synthetic reasoning trace wrapped in <think>...</think>.

💬 Simplified example

A typical example follows this structure:

{
  "messages": [
    {
      "role": "system",
      "content": "Generate a coherent reasoning trace using only the question and final answer. Do not rely on reasoning bubbles or pre-existing intermediate reasoning. Keep the trace concise and output only the trace inside <think>...</think>."
    },
    {
      "role": "user",
      "content": "Question: Lyka needs $120 more to buy a $160 smartphone. She has $40 and plans to save over 8 weeks.\n\nFinal answer: $15 per week.\n\nGenerate a reasoning trace connecting the question to the final answer."
    },
    {
      "role": "assistant",
      "content": "<think>Lyka needs $160 - $40 = $120 more to buy the smartphone. To save this amount over 8 weeks, divide $120 by 8, giving $15 per week.</think>"
    }
  ]
}

This example is abbreviated for readability. Actual records contain the full task instructions and generated reasoning traces, with each target trace limited to 1,200 characters.

📊 Dataset composition

Split	Examples	File
Training	10,000	train_reasoning_generation.jsonl
Evaluation	500	eval_reasoning_generation.jsonl
Total	10,500

Both splits follow the same message structure. The training set is intended for supervised fine-tuning, while the held-out evaluation set supports assessing reasoning-generation quality.

🔧 Processing

• The source dataset is saidutta69/qwen3.8-max-distillation-50k-clean, whose teacher is Qwen3.8-Max (qwen3.8-max-preview).
• 10,000 entries were randomly sampled for training and 500 entries for evaluation.
• The task was inverted so that only the question and final answer are provided as input, without reasoning bubbles.
• Generated target reasoning traces are limited to 1,200 characters.

This setup encourages models to reconstruct plausible reasoning from minimal information rather than expand supplied intermediate reasoning. The emphasis is on concise, answer-conditioned explanations suitable for reasoning distillation and supervised fine-tuning.

🎯 Intended uses

• Training smaller language models to generate reasoning traces from questions and final answers.
• Research into trace inversion and reasoning distillation using Qwen3.8-Max-derived data.
• Supervised fine-tuning for concise, structured reasoning generation without reasoning bubbles.
• Exploring how models can reconstruct plausible explanations when intermediate reasoning information is unavailable.

⚠️ Limitations

The generated traces are synthetic reconstructions, not verified records of Qwen3.8-Max's original internal reasoning. They may contain mistakes, omit relevant steps, or rationalize an incorrect final answer.

Because the final answer is supplied as input, a plausible-looking trace does not demonstrate that the model independently derived the answer or that every step is valid. Trace quality, logical consistency, and answer correctness should be evaluated separately.

The 1,200-character limit favors concise explanations and may restrict the level of detail possible for complex problems. The dataset also inherits the domain distribution and potential benchmark contamination of its upstream source. The evaluation split should therefore not be assumed to provide contamination-free measurements of general reasoning ability.

⚖️ Licensing

The source dataset declares its license as other and documents restrictions inherited from Alibaba Cloud Model Studio terms, as well as noncommercial licensing obligations associated with some upstream sources.

Users should review the source dataset card, its provenance documentation, and all applicable source-model and upstream dataset terms before training, redistribution, or commercial use. The derived dataset should not be assumed to have unrestricted usage rights merely because it is publicly available.

🔗 References

• xtools-at/Qwen3.5-4B-Reasoning-Generator
• xtools-at/Qwen3.5-4B-Trace-Inverter
• saidutta69/qwen3.8-max-distillation-50k-clean
• r0b0tlab/qwen3.8-max-distillation-50k
• Zhang, Morris, and Shmatikov, How to Steal Reasoning Without Reasoning Traces, 2026.

@misc{zhang2026stealreasoning,
  title         = {How to Steal Reasoning Without Reasoning Traces},
  author        = {Tingwei Zhang and John X. Morris and Vitaly Shmatikov},
  year          = {2026},
  eprint        = {2603.07267},
  archivePrefix = {arXiv}
}