Datasets:

Chia-Mu-Lab
/

REP-datasets

license: apache-2.0
language:
  - en
task_categories:
  - text-generation
tags:
  - reasoning
  - distillation
  - reasoning-trace-exposure
  - rep
  - openthoughts
size_categories:
  - 10K<n<100K
configs:
  - config_name: distill_q3_14b_clean
    data_files: data/distill_q3_14b_clean/*
  - config_name: distill_q3_14b_original
    data_files: data/distill_q3_14b_original/*
  - config_name: distill_q3_32b_clean
    data_files: data/distill_q3_32b_clean/*
  - config_name: distill_q3_32b_original
    data_files: data/distill_q3_32b_original/*

REP-datasets — reasoning traces exposed by REP, used to train the released students

Released with "Hidden Thoughts Are Not Secret: Reasoning-Trace Exposure in LLMs" (EMNLP 2026, arXiv:2606.00642). Code: https://github.com/0x-yuan/REP · Models: Chia-Mu-Lab/REP-models.

Each config is the exact training set of one released student. Questions come from OpenThoughts-114k (math); traces were exposed from open-weight victims (Qwen3-14B / Qwen3-32B) with the REP prompt and are the visible-channel output only.

Config	Rows	Victim	Filter	Trains
`distill_q3_14b_clean`	10 000	Qwen3-14B	structural ✓ + answer-correct	`REP-models/qwen25-7b-rep-q3_14b-clean`
`distill_q3_14b_original`	8 046	Qwen3-14B	structural (no answer check)	`REP-models/qwen25-7b-rep-q3_14b-original`
`distill_q3_32b_clean`	10 000	Qwen3-32B	structural ✓ + answer-correct	`REP-models/qwen25-7b-rep-q3_32b-clean`
`distill_q3_32b_original`	6 291	Qwen3-32B	structural (no answer check)	`REP-models/qwen25-7b-rep-q3_32b-original`

Columns: question, r1 (victim's internal trace), r2 (the exposed trace), answer, completion (the student target).

Responsible use

Research artifact. Do not redistribute exposed traces as training data for a competing product; see ETHICS.md in the code release.

Citation

@inproceedings{lu2026hiddenthoughts,
  title     = {Hidden Thoughts Are Not Secret: Reasoning-Trace Exposure in LLMs},
  author    = {Lu, Yu-An and Tsai, Ci-Yang and Tsai, Yu-Lin and Popa, Raluca Ada and Yu, Chia-Mu},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year      = {2026},
  note      = {arXiv:2606.00642}
}