| --- |
| 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. |
|
|
