README
SYNTH
SYNTH is the first open generalist synthetic dataset for training small reasoning model end-to-end, jointly released by Pleias and the AI Alliance.
SYNTH includes 79,648,272 individual text samples, comprising over 41 billion words (about 75 billion tokens with Pleias tokenizer). It is based on the amplification of 58,698 articles from Wikipedia and made possible thanks to the Structured Wikipedia dataset from Wikimedia Enterprise.
SYNTH differs from existing open synthetic dataset in being:
- fully open based on seed text under open license (CC-By-SA) and generated with models allowing for output reuse. This means that SYNTH can be universally release and serve as a basis for further reproducible synthetic pipelines.
- state of the art for small models below 350 million parameters. We release two models train on SYNTH achieving current best results for size range on MMLU and other standard evaluation metrics.
- data efficient with best results attained with only 100-200 billions tokens trained on SYNTH.
- reasoning by design with all generated answers being accompanied with intermediary reasoning traces in an entirely new syntax.
- diverse comprising a wide range of exercises that cover many use cases of small models: retrieval-augmented generation, creative writing, arithmetics, information extraction, etc.
- multilingual with about 20% of all texts in other languages than English, for now limited on European languages (German, French, Spanish, Italian, Polish, Dutch, Latin).
SYNTH is not only the name of a dataset but an initiative for open synthetic data and open environment led by AI Alliance and Pleias that aims to address the critical gap in open-source AI development by creating a cutting-edge, open-source data corpus for training sovereign AI models and advanced AI agents.
Dataset Design
Amplified knowledge
At its core, SYNTH is a fully synthetic and engineered corpus derived from a sample of 50,000 pages curated by the Wikipedia community. Throughout the past two decades, thousands of contributors selected a collection of core topics that every encyclopedia should have, Wikipedia:Vital articles. It’s a concentric selection starting at level 1 (10 articles) up to level 5 (50,000 articles). SYNTH includes as its starting point all articles featured in level 5.
SYNTH further expands on this core nucleus with three additional seed collections:
- specialized articles: following on intermediary evaluation, we added 8,698 articles to reinforce coverage of specific fields like law, medicine, chemistry. Selection was based on category tree search analysis and aimed to fill remaining holes in knowledge coverage from Wikipedia:Vital articles.
- textbooks: wikipedia articles are focused on encyclopedic knowledge but lag on practical knowledge and how to, which happens to be the focus of another Wikimedia project, Wikibooks. For now we included 3,727 pages on cooking from Wikibooks but looking forward to expand on additional forms of experential knowledge (gardening, language acquisition, etc.)
- recent/self knowledge: we incorporated a small sample of 130 texts hand-crafted internally to expand model familiarity with recent events, self-awareness about training condition and general research information on AI. This collection has been highly amplified.
This content act as the SYNTH memory base and has been amplified at a minimum 100 times (about 10000 times for recent/self knowledge). Our amplification strategy relies on a new synthetic pipeline, partly inspired by RAG applications:
- Selection of individual consistent sections from the original articles (about 250,000 for the core sample of 50,000 pages).
- Generation of queries with randomized constraints for style variation, query outcomes. It proved especially determining to have enough negative queries to reinforce world knowledge and limit hallucinations.
Synthetic exercises
The approach has been originally explored by Pleias for retrieval-augmented generation. It has been extended to virtually most of the expected use case of small reasoning models:
- arithmetics
- creative writing We injected randomized constraints
Dataset Details
Dataset Description
- Curated by: Wikipedia community (Wikipedia:Vital Articles) and Pleias.
- Funded by [optional]: Pleias
- Shared by [optional]: Pleias
- Language(s) (NLP): English (80%), French, German, Italian, Spanish, Polish, Dutch and Latin.
- License:
Dataset Sources [optional]
While the final training data is fully synthetic, it relied on seeds collected from three data sources:
- Structured Wikipedia: We used directly the dumps made available by the Wikimedia Foundation.
- Wikibooks: extracted through the official Wikimedia API.
- Internal documents from Pleias: mostly model-self documentation and few updated information.
Uses
The dataset aims to support data efficient training of small reasoning model. It provide a generalist, self-sufficient collection of multilingual amplified encyclopedic texts along with synthetic reasoning traces, as well as synthetic tasks that reinforce most of the expected capacities of small model.
In contrast with organic pretraining dataset, SYNTH allows for fast convergence to the existing SOTA (about 100 billion tokens). Furthermore, SYNTH is fully releasable, only use sourced text under free license.
Overall, SYNTH aims to support an emerging ecosystem of small training model by providing a reusable generalist foundational dataset.
Direct Use
Direct use include:
- Pretraining of small reasoning models: the dataset is sufficient to elicit most expected capacities in small models.
- Mid-training/fine-tuning of existing models: we already led successful experiments with Pleias-350m.
- Research/explainability experiment: with its openness and data efficiency, SYNTH should be an ideal resource for research on model memorization or skill acquisition.
Out-of-Scope Use
Current out-of-scope use include:
- Code generation: we intently excluded code data from SYNTH as this would require the development of specific synthetic pipeline.
- Global multilingual support: SYNTH only claims support from our current list of eight languages.
- Training of large models: the difficulty of synthetic exercises has been calibrated for models smaller than a few billion parameters.
Yet, SYNTH is a live resources and we intend to cover some of these use cases in future releases.
Dataset Structure
| Field | Type | Description |
|---|---|---|
| synth_id | string | Unique synthetic identifier for each generated sample. |
| language | string | Language of the text sample (e.g., "en", "fr", "it", "es", "de", "pl", "nl", "la"). |
| exercise | string | Type of synthetic exercise (e.g., reasoning, writing, retrieval, arithmetic). Describes the synthetic task context. |
| model | string | Finetuned model used to generate the synthetic sample |
| query | string | Backtranslated query. |
| query_seed_url | string | URL of the Wikipedia or Wikibooks section that served as the seed for query generation. |
| query_seed_text | string | Extend text used as seed for query generation. |
| additional_seed_url | string | Optional additional URL(s) used as supplementary seed |
| seed_license | string | License of the seed text (most of the time "CC-BY-SA 4.0"). |
| constraints | string | Generation constraints applied to answer generation. Varies depending on the exercise |
| script | string | Internal template or script identifier defining the structure of the synthetic exercise. |
| synthetic_reasoning | string | Generated reasoning draft. |
| synthetic_answer | string | Final generated answer or output corresponding to the query. |
| words | int64 | Word count of the full generated text sample (query + draft + answer) |
Dataset Creation
Curation Rationale
SYNTH is structured around a “memory core”, the Wikipedia vital articles.. Throughout the past two decades, thousands of contributors selected a collection of core topics that every encyclopedia should have: it’s a concentric selection starting at level 1 (10 articles) up to level 5 (50,000 articles). SYNTH includes as its starting point all articles featured in level 5. It further expands on this selection by increasing coverage of more specialized domains (physics, chemistry, law…) through targeted expansion of wikidata knowledge graphs.
Source Data
The 58,698 Wikipedia articles were collected thanks to ''Structured Wikipedia'', a project from Wikimedia Enterprise that parsed directly rendered Wikipedia articles in html. Structured Wikipedia fixed most of the formatting issues linked with the mediawiki syntax and provides a clean, section-based version of all Wikipedia pages.
We additionally extracted 3,000 cooking recipes from Wikibooks using the standard API method from Wikimedia.
Data Collection and Processing
Who are the source data producers?
The main sourced dataset used for synthetic amplification was curated by the English Wikipedia communities throughout nearly 2 decades. Rationale for selection are available on the relevant talk pages of Wikipedia:Vital articles.
The selection reflect similar bias for "canon" general knowledge in English-speaking countries than major LLM benchmarks like MMLU (drawn from high school exams).
Personal and Sensitive Information
The dataset only contain encyclopedic information on highly well-known historical people. No PII curation was needed.
Bias, Risks, and Limitations
The dataset was created from a collection of 50,000 Wikipedia articles curated by the community (Wikipedia:Vital Articles).
On top of the well documented structural bias in Wikipedia contribution and editing, the selection has been intently made from the perspective of western US/European culture.
Due to systematic Wikipedia grounding, the data presents a very low risk of toxic or problematic content, as well as poor or highly hallucinated information.
Similar repos
README
SYNTH
SYNTH is the first open generalist synthetic dataset for training small reasoning model end-to-end, jointly released by Pleias and the AI Alliance.
SYNTH includes 79,648,272 individual text samples, comprising over 41 billion words (about 75 billion tokens with Pleias tokenizer). It is based on the amplification of 58,698 articles from Wikipedia and made possible thanks to the Structured Wikipedia dataset from Wikimedia Enterprise.
SYNTH differs from existing open synthetic dataset in being:
- fully open based on seed text under open license (CC-By-SA) and generated with models allowing for output reuse. This means that SYNTH can be universally release and serve as a basis for further reproducible synthetic pipelines.
- state of the art for small models below 350 million parameters. We release two models train on SYNTH achieving current best results for size range on MMLU and other standard evaluation metrics.
- data efficient with best results attained with only 100-200 billions tokens trained on SYNTH.
- reasoning by design with all generated answers being accompanied with intermediary reasoning traces in an entirely new syntax.
- diverse comprising a wide range of exercises that cover many use cases of small models: retrieval-augmented generation, creative writing, arithmetics, information extraction, etc.
- multilingual with about 20% of all texts in other languages than English, for now limited on European languages (German, French, Spanish, Italian, Polish, Dutch, Latin).
SYNTH is not only the name of a dataset but an initiative for open synthetic data and open environment led by AI Alliance and Pleias that aims to address the critical gap in open-source AI development by creating a cutting-edge, open-source data corpus for training sovereign AI models and advanced AI agents.
Dataset Design
Amplified knowledge
At its core, SYNTH is a fully synthetic and engineered corpus derived from a sample of 50,000 pages curated by the Wikipedia community. Throughout the past two decades, thousands of contributors selected a collection of core topics that every encyclopedia should have, Wikipedia:Vital articles. It’s a concentric selection starting at level 1 (10 articles) up to level 5 (50,000 articles). SYNTH includes as its starting point all articles featured in level 5.
SYNTH further expands on this core nucleus with three additional seed collections:
- specialized articles: following on intermediary evaluation, we added 8,698 articles to reinforce coverage of specific fields like law, medicine, chemistry. Selection was based on category tree search analysis and aimed to fill remaining holes in knowledge coverage from Wikipedia:Vital articles.
- textbooks: wikipedia articles are focused on encyclopedic knowledge but lag on practical knowledge and how to, which happens to be the focus of another Wikimedia project, Wikibooks. For now we included 3,727 pages on cooking from Wikibooks but looking forward to expand on additional forms of experential knowledge (gardening, language acquisition, etc.)
- recent/self knowledge: we incorporated a small sample of 130 texts hand-crafted internally to expand model familiarity with recent events, self-awareness about training condition and general research information on AI. This collection has been highly amplified.
This content act as the SYNTH memory base and has been amplified at a minimum 100 times (about 10000 times for recent/self knowledge). Our amplification strategy relies on a new synthetic pipeline, partly inspired by RAG applications:
- Selection of individual consistent sections from the original articles (about 250,000 for the core sample of 50,000 pages).
- Generation of queries with randomized constraints for style variation, query outcomes. It proved especially determining to have enough negative queries to reinforce world knowledge and limit hallucinations.
Synthetic exercises
The approach has been originally explored by Pleias for retrieval-augmented generation. It has been extended to virtually most of the expected use case of small reasoning models:
- arithmetics
- creative writing We injected randomized constraints
Dataset Details
Dataset Description
- Curated by: Wikipedia community (Wikipedia:Vital Articles) and Pleias.
- Funded by [optional]: Pleias
- Shared by [optional]: Pleias
- Language(s) (NLP): English (80%), French, German, Italian, Spanish, Polish, Dutch and Latin.
- License:
Dataset Sources [optional]
While the final training data is fully synthetic, it relied on seeds collected from three data sources:
- Structured Wikipedia: We used directly the dumps made available by the Wikimedia Foundation.
- Wikibooks: extracted through the official Wikimedia API.
- Internal documents from Pleias: mostly model-self documentation and few updated information.
Uses
The dataset aims to support data efficient training of small reasoning model. It provide a generalist, self-sufficient collection of multilingual amplified encyclopedic texts along with synthetic reasoning traces, as well as synthetic tasks that reinforce most of the expected capacities of small model.
In contrast with organic pretraining dataset, SYNTH allows for fast convergence to the existing SOTA (about 100 billion tokens). Furthermore, SYNTH is fully releasable, only use sourced text under free license.
Overall, SYNTH aims to support an emerging ecosystem of small training model by providing a reusable generalist foundational dataset.
Direct Use
Direct use include:
- Pretraining of small reasoning models: the dataset is sufficient to elicit most expected capacities in small models.
- Mid-training/fine-tuning of existing models: we already led successful experiments with Pleias-350m.
- Research/explainability experiment: with its openness and data efficiency, SYNTH should be an ideal resource for research on model memorization or skill acquisition.
Out-of-Scope Use
Current out-of-scope use include:
- Code generation: we intently excluded code data from SYNTH as this would require the development of specific synthetic pipeline.
- Global multilingual support: SYNTH only claims support from our current list of eight languages.
- Training of large models: the difficulty of synthetic exercises has been calibrated for models smaller than a few billion parameters.
Yet, SYNTH is a live resources and we intend to cover some of these use cases in future releases.
Dataset Structure
| Field | Type | Description |
|---|---|---|
| synth_id | string | Unique synthetic identifier for each generated sample. |
| language | string | Language of the text sample (e.g., "en", "fr", "it", "es", "de", "pl", "nl", "la"). |
| exercise | string | Type of synthetic exercise (e.g., reasoning, writing, retrieval, arithmetic). Describes the synthetic task context. |
| model | string | Finetuned model used to generate the synthetic sample |
| query | string | Backtranslated query. |
| query_seed_url | string | URL of the Wikipedia or Wikibooks section that served as the seed for query generation. |
| query_seed_text | string | Extend text used as seed for query generation. |
| additional_seed_url | string | Optional additional URL(s) used as supplementary seed |
| seed_license | string | License of the seed text (most of the time "CC-BY-SA 4.0"). |
| constraints | string | Generation constraints applied to answer generation. Varies depending on the exercise |
| script | string | Internal template or script identifier defining the structure of the synthetic exercise. |
| synthetic_reasoning | string | Generated reasoning draft. |
| synthetic_answer | string | Final generated answer or output corresponding to the query. |
| words | int64 | Word count of the full generated text sample (query + draft + answer) |
Dataset Creation
Curation Rationale
SYNTH is structured around a “memory core”, the Wikipedia vital articles.. Throughout the past two decades, thousands of contributors selected a collection of core topics that every encyclopedia should have: it’s a concentric selection starting at level 1 (10 articles) up to level 5 (50,000 articles). SYNTH includes as its starting point all articles featured in level 5. It further expands on this selection by increasing coverage of more specialized domains (physics, chemistry, law…) through targeted expansion of wikidata knowledge graphs.
Source Data
The 58,698 Wikipedia articles were collected thanks to ''Structured Wikipedia'', a project from Wikimedia Enterprise that parsed directly rendered Wikipedia articles in html. Structured Wikipedia fixed most of the formatting issues linked with the mediawiki syntax and provides a clean, section-based version of all Wikipedia pages.
We additionally extracted 3,000 cooking recipes from Wikibooks using the standard API method from Wikimedia.
Data Collection and Processing
Who are the source data producers?
The main sourced dataset used for synthetic amplification was curated by the English Wikipedia communities throughout nearly 2 decades. Rationale for selection are available on the relevant talk pages of Wikipedia:Vital articles.
The selection reflect similar bias for "canon" general knowledge in English-speaking countries than major LLM benchmarks like MMLU (drawn from high school exams).
Personal and Sensitive Information
The dataset only contain encyclopedic information on highly well-known historical people. No PII curation was needed.
Bias, Risks, and Limitations
The dataset was created from a collection of 50,000 Wikipedia articles curated by the community (Wikipedia:Vital Articles).
On top of the well documented structural bias in Wikipedia contribution and editing, the selection has been intently made from the perspective of western US/European culture.
Due to systematic Wikipedia grounding, the data presents a very low risk of toxic or problematic content, as well as poor or highly hallucinated information.