README.md

---
pretty_name: StateBench v1
task_categories:
- text-generation
language:
- en
tags:
- synthetic
- state-space-models
- recurrent-models
- pretraining
- sft
- sdft
size_categories:
- 10M<n<100M
configs:
- config_name: default
  data_files:
  - split: train
    path: "data/train/*"
  - split: validation
    path: "data/validation/*"
- config_name: dense-100m
  data_files:
  - split: train
    path: "data/dense-100m/train/*"
  - split: validation
    path: "data/dense-100m/validation/*"
---

<div align="center">

# StateBench v1

### 110 million verified state episodes for pretraining, SFT, and on-policy SDFT

Train models to **retain**, **edit**, **address**, and **transform** state across long execution traces.

</div>

## Choose a configuration

| Configuration | Scale | Use when |
|:--|:--|:--|
| **default** | 10,000,000 programs · 7.80 GiB | You need 36 explicit families and varied surfaces |
| **dense-100m** | 100M episodes · 98.61 GiB | You need high-throughput state training with grounded, structurally varied prompts |

All configurations contain structured prompt, completion, and teacher-only privileged context. Every target is computed by an executable reference machine, never authored by a teacher model.

## Load

    from datasets import load_dataset

    default = load_dataset(
        "aabbdev/StateBench-v1", "default", split="train", streaming=True
    )
    dense = load_dataset(
        "aabbdev/StateBench-v1", "dense-100m", split="train", streaming=True
    )

| Configuration | Train | Validation |
|:--|--:|--:|
| default | 9,949,600 rows | 50,400 rows |
| dense-100m | 24,875,000 rows | 125,000 rows |

## Train three ways

### SFT

    sft = dense.select_columns(["prompt", "completion"])

### On-policy SDFT with TRL

    sdft = dense.select_columns(["prompt", "privileged_context"])

privileged_context is visible only to the teacher. Never concatenate it into the student prompt. It contains the verified rendered target expected by TRL SDFTTrainer.

### Causal pretraining

The default configuration includes a ready-to-use text column. Dense configurations avoid storing a duplicate text view; derive it while streaming:

    def to_text(row):
        prompt = row["prompt"][0]["content"]
        answer = row["completion"][0]["content"]
        return {"text": f"User:\n{prompt}\n\nAssistant:\n{answer}"}

    dense_pretrain = dense.map(to_text)

## Coverage map

### 16 primitive domains

| Area | Domains |
|:--|:--|
| Memory | Retention & lifecycle · Capacity & eviction · Addressing & aliasing · Editing & transactions |
| Control | Selectivity & access · Interference & concurrency · Automata & control · Algebra & reversibility |
| Structures | Data structures · Graphs & spatial state · Temporal streaming · Online adaptation |
| Systems | Distributed replication · Agents & messaging · Integrity & error control · Runtime state |

### 8 composition domains

| Area | Compositions |
|:--|:--|
| State systems | Transactional + temporal · Adaptive + control · Concurrent + distributed · Graph + structures |
| Stress systems | Robust streaming · Multi-agent state · Algebraic automata · Memory pressure |

default implements 36 explicit behavioral families. The corrected dense-100m v1.4 configuration rematerializes 100M verified trajectories through 24 domain dialects: the 16 primitive domains and 8 compositions above. Its 32 parseable document structures and 64 distinct metamorphic surface passes are physically present in the prompts, including data formats, code-like records, logs, traces, conversations, and nested documents.

The dense semantic kernel trains long-horizon set, remove, additive, multiplicative, observation, and no-op transitions. Its 384 balanced `families` values are curriculum labels spanning domain and profile combinations, not 384 different VM mechanisms. Domain dialects diversify how transitions are presented; they do not turn each label into a full simulator of that real-world system. StateBench remains synthetic state training, not a factual natural-language corpus.

## Expected model capabilities

| Capability | Training pressure |
|:--|:--|
| Working-memory utilization | Retain independent values across long active traces |
| Precise state mutation | Write, overwrite, delete, add, and scale without collateral damage |
| Associative addressability | Resolve exact, hierarchical, indirect, and collision-prone keys |
| Algorithmic recurrence | Execute compact transition sequences over long horizons |
| In-context rule use | Apply a supplied transition legend without updating model weights |
| Interference control | Protect persistent state from noise, churn, concurrent writes, and repeated reads |
| Surface robustness | Preserve semantics across structured documents, logs, code-like records, and conversations |
| Streaming stability | Preserve behavior across long traces and packed independent episodes |

StateBench trains state handling, not factual knowledge. It does not by itself expand a model's physical context window, add world knowledge, or guarantee general reasoning improvements outside state-intensive tasks.

## Data contract

| Field | Availability | Purpose |
|:--|:--|:--|
| prompt | all configs | Student-visible conversational input |
| completion | all configs | Verified SFT target |
| privileged_context | all configs | Teacher-only SDFT context |
| text | default; derived for dense | Causal pretraining view |
| semantic identity | 1 default; 4 dense | Deduplication and audit |
| family / macro / level | scalar default; lists dense | Curriculum filtering |
| density_units_per_1k_chars | dense configs | Semantic density control |

## Dense configurations at a glance

| Configuration | Density | Token estimate |
|:--|--:|--:|
| dense-100m | 160.04 units / 1k chars | 106.6B GPT-2 · 118.9B Qwen3 |

The token estimates were measured on 1,000 v1.4 packed rows (seed 42). Actual training cost depends on chat templates, truncation, packing, and the SDFT generation policy.

## Integrity

- 110M semantic identities are unique within their respective configurations.
- Required fields, roles, answer JSON, and SDFT isolation passed full-corpus audits.
- No benchmark name, family label, or privileged answer appears in model-visible prompts.
- SHA-256 checksums cover every Parquet shard.
- The public training corpus is separate from private StateBench evaluation material.

Release manifests: dataset_manifest.json and dense-100m-manifest.json.