aabbdev/StateBench-v1

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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.

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Choose a configuration

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

Train three ways

SFT

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

On-policy SDFT with TRL

sdft = dense.selectcolumns(["prompt", "privilegedcontext"])

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}"}

densepretrain = dense.map(totext)

Coverage map

16 primitive domains

8 composition domains

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

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

Dense configurations at a glance

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.