| --- |
| license: unknown |
| language: |
| - en |
| - fr |
| - ar |
| - hi |
| - es |
| - zh |
| tags: |
| - dpo |
| - preference-learning |
| - reasoning |
| - chain-of-thought |
| - multilingual |
| - post-training |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| <img src="adaption_banner.png" alt="Adaption" width="600"> |
|
|
| # Reasoning-Augmented Preference Pairs |
|
|
| **140,582 preference pairs across 6 languages and 8 domains, each chosen response paired with a step-by-step reasoning trace.** |
|
|
| Every row includes a `chosen`/`rejected` preference pair and a reasoning trace behind the chosen response. This supports DPO training on the preference signal and reasoning distillation from the traces, together or separately. Rows come from 13 public instruction and reasoning datasets, filtered to HARD and MEDIUM difficulty, with the five non-English languages holding roughly 10% each. |
|
|
| ## Highlights |
|
|
| - **Reasoning traces.** Each row pairs `chosen`/`rejected` with a `reasoning_trace` for the chosen response. The trace explains the preferred answer, not the rejected one. |
| - **Difficulty filtered.** HARD and MEDIUM rows only. EASY rows are dropped during selection. |
| - **Multilingual.** English plus Arabic, French, Hindi, Mandarin, and Spanish, at roughly 10% each for the five non-English languages. |
| - **Source context.** 5 of the 13 sources include the underlying `context` (financial filing, clinical record, article) in the same language as the prompt. |
| - **Domain coverage.** 8 buckets: general instruction-following, medical, finance, science, math, code, safety, and other. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("your-org/preference-pairs") # replace with the published repo id |
| print(ds["train"][0]) |
| ``` |
|
|
| ## Dataset summary |
|
|
| | | | |
| |---|---| |
| | **Rows** | 140,582 | |
| | **Columns** | `prompt`, `context`, `chosen`, `rejected`, `reasoning_trace`, `language`, `domain`, `source_dataset` | |
| | **Languages** | English + Arabic, French, Hindi, Mandarin, Spanish (~10% each) | |
| | **Domains** | 8 buckets: general instruction-following, medical, finance, science, math, code, safety, other (legal and summarization fold into `other`) | |
| | **Difficulty** | HARD and MEDIUM only | |
|
|
| ## Columns |
|
|
| | Column | Description | |
| |---|---| |
| | `prompt` | The task prompt, as sourced from the originating dataset. No separate rewrite or enhancement step this round (see [What's different from v1](#whats-different-from-v1)). | |
| | `context` | Supporting source document or data referenced by the prompt (financial filing, clinical record, article, etc.), in the same language as `prompt`. Present for 5 of the 13 sources. | |
| | `chosen` | The preferred response to the prompt. | |
| | `rejected` | The dispreferred response to the same prompt. Pair with `chosen` for DPO-style preference training. | |
| | `reasoning_trace` | A step-by-step reasoning trace behind the `chosen` response specifically, not `rejected`. | |
| | `language` | Detected language of `prompt` (ISO 639-1 code where available). | |
| | `domain` | Coarse subject-area bucket derived from the originating source dataset. | |
| | `source_dataset` | Which of the 13 public datasets the row came from. | |
|
|
| ## Curation recipe |
|
|
| 1. **Source aggregation.** 13 public datasets spanning general instruction-following, medical calculation, finance QA, science, math, code, legal scenarios, summarization, and safety (see [Source composition](#source-composition)). |
| 2. **Difficulty-based selection.** HARD and MEDIUM only. EASY rows are dropped. |
| 3. **Reasoning + preference pairs.** Each row carries a `chosen`/`rejected` pair plus a `reasoning_trace` behind the chosen response. |
|
|
| ## Distributions |
|
|
| **Language** |
|
|
| <img src="./language_distribution.png" alt="Language distribution" width="400"> |
|
|
| **Domain** |
|
|
| <img src="./domain_distribution.png" alt="Domain distribution" width="400"> |
|
|
| ## Source composition |
|
|
| | Source dataset | Domain bucket | Rows kept | |
| |---|---|---| |
| | WizardLM/WizardLM_evol_instruct_V2_196k | general | 52,143 | |
| | ncbi/MedCalc-Bench-v1.2 | medical | 23,373 | |
| | ibm-research/finqa | finance | 16,598 | |
| | nvidia/Nemotron-Science-v1 | science | 14,899 | |
| | AI-MO/NuminaMath-CoT | math | 8,982 | |
| | nvidia/Nemotron-SFT-v3-IF | general | 6,952 | |
| | nvidia/Nemotron-Competitive-Programming-v1 | code | 4,390 | |
| | qiaojin/PubMedQA | medical | 4,130 | |
| | nvidia/Nemotron-SFT-Safety-v1 | safety | 3,773 | |
| | nvidia/Nemotron-Math-v2 | math | 3,005 | |
| | ise-uiuc/Magicoder-OSS-Instruct-75K | code | 1,380 | |
| | relai-ai/legal-scenarios-SCOTUS-2024-decisions | legal | 491 | |
| | EdinburghNLP/xsum | summarization | 466 | |
| | **Total** | | **140,582** | |
|
|
| ## License and attribution |
|
|
| This dataset aggregates 13 public source datasets, each under its own license. Downstream use is subject to the most restrictive applicable source license. Confirm and list per-source terms before publishing. |
|
|
|
|
