README.md

---
language:
- en
license: apache-2.0
tags:
- synthetic
- pretraining
- text-generation
- language-modeling
task_categories:
- text-generation
task_ids:
- language-modeling
size_categories:
- 1M<n<10M
pretty_name: SYN-1B
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: validation
    path: data/validation-*.parquet
train-eval-index:
- config: default
  task: text-generation
  task_id: language-modeling
  splits:
    train_split: train
    eval_split: validation
  col_mapping:
    text: text
---

# SYN-1B

<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/CsFvFjDP27FD0M1AhlWA1.png" width="600" />

## Dataset Summary

SYN-1B is a 1.04B-token synthetic language-modeling corpus of rule-governed
text streams. Each row is a decoded instance in which the text establishes
facts, mappings, bindings, or simple generative rules; later spans may revise
those rules, swap bindings, delay a query over long filler, or present a null
control with event-like surface text that should not change the answer.

The dataset is intended as structured pretraining data, not as a supervised
instruction dataset. It uses ordinary next-token prediction, but the examples
are generated from known latent state machines. That makes it useful when a
researcher wants training data where long-context behavior, variable binding,
belief revision, recency, distractor robustness, and held-out transfer can be
measured exactly rather than inferred from natural text.

The [data/](data/) Parquet files expose one decoded synthetic instance per row
in the `text` column. The original packed `uint16` token shards, sidecars, and
QA manifests are kept under [raw/](raw/) for exact token-level training and
audit use. The Hugging Face `validation` split corresponds to the SYN
generator's `eval` split. F5 appears only in that held-out eval/validation
split.

## Why Pretrain With It?

SYN-1B gives language models repeated practice on behaviors that are important
in real use but sparse, ambiguous, or hard to label in web text:

- Tracking a rule or binding across many intervening tokens.
- Updating an answer after a correction instead of copying the most recent
  local surface pattern.
- Preserving unchanged facts when only part of a rule changes.
- Distinguishing real state-changing events from plausible-looking distractors.
- Answering from evidence that may be hundreds or thousands of tokens old.
- Transferring the same abstract update pattern to a held-out surface format.

For general pretraining, SYN-1B can be mixed as a small synthetic fraction
alongside web, education, code, or domain corpora to add dense, auditable
examples of state tracking and revision. It is especially relevant for work on
long-context models, recurrent or memory-augmented models, retrieval-free
reasoning, continual belief updating, and curricula that target systematic
generalization.

## Other Applications

Beyond pretraining, SYN-1B can be used as:

- A controlled benchmark for long-context recall and rule updating.
- A source of probing data for whether hidden states encode active mappings,
  bindings, recency, and corrected values.
- A stress test for distractor sensitivity and surface-marker shortcuts.
- A data generator baseline for synthetic-curriculum, data-mixture, and
  scaling-law studies.
- A reproducible corpus for mechanistic interpretability, because the raw
  sidecars under [raw/sidecars/](raw/sidecars/) identify events, writes, query
  positions, answer positions, evidence spans, composition depth, controlled gap
  lengths, and held-out families.

## Limitations

SYN-1B is synthetic by design. It should not be treated as a substitute for
natural-language pretraining data, an instruction-following dataset, or a
factuality benchmark. The task families cover a controlled set of rule-updating
and long-context patterns, so improvements on SYN-1B should be checked against
naturalistic and out-of-distribution evaluations before making broad claims.

The decoded Parquet view is convenient for Hugging Face tooling, while the
packed token shards and sidecars in [raw/](raw/) are the reference artifacts for
exact token-level audits and reproduction. Users who need full supervision
metadata should read the sidecars rather than relying only on the compact
Parquet columns.

## Task Families

The task families isolate different forms of state tracking:

| Family | Role |
| --- | --- |
| F1 branch reversal | A rule holds, then an event changes the active mapping. |
| F2 binding swap | Entity-to-attribute bindings are corrected by swaps rather than a single surface marker. |
| F3 delayed correction / recall | Old evidence and controlled evidence age test whether stored evidence survives and can be reinterpreted. |
| F4 flat null | No interpretive event occurs; event-like distractors test false-positive rule updates. |
| F5 modular-stream switch | Eval-only transfer task with no training exposure, used to test transfer beyond memorized surface formats. |

## Source and Reproducibility

The generator, QA suite, and build specification are available in the
[source repository](https://github.com/quixiai/aum). See
[SYN-1B.md](https://github.com/quixiai/aum/blob/main/SYN-1B.md) for the corpus
specification.

## How to Load

```python
from datasets import load_dataset

ds = load_dataset("QuixiAI/SYN-1B")
```

## Dataset Stats

| Split | Rows | Synthetic instance tokens in raw build |
| --- | ---: | ---: |
| `train` | 4,063,978 | 1,000,001,463 |
| `validation` | 178,863 | 40,002,882 |
| `total` | 4,242,841 | 1,040,004,345 |

The Hugging Face dataset is decoded text. Consumers can tokenize the `text`
column with any tokenizer. The token counts above refer to the reference raw
build in [raw/](raw/), which was generated and QA-audited with the
`HuggingFaceTB/SmolLM2-135M` tokenizer.

## Data Schema

Columns:

- `text`: decoded synthetic instance text.
- `instance_id`, `family`, `syn_split`: stable identifiers and SYN family.
- `token_len`, `task_token_count`, `filler_token_count`, `controlled_gap_tokens`,
  `composition_depth`, `num_queries`: compact per-instance metadata.
- `shard`, `window_index`, `start_offset`, `token_hash`: join keys back to the
  packed raw token stream and sidecar records under [raw/](raw/).

## Licensing Information

License: Apache-2.0