Genesis-AI-Labs/tinyvm-tier1

Tiny-VM Tier 1 — Register Traces

Synthetic dataset of straight-line Tiny-VM programs with full execution traces and pre-rendered training prompts. Tier 1 of the FANC "Latent State as Computer" experimental curriculum, focused on register-file tracking under bounded program length.

• 200,000 train programs
• 140,000 stratified eval programs (7 buckets × 20,000, one per program length n ∈ {8, 16, 32, 48, 64, 96, 128})
• Generator: tinyvm.generators.gen_register_trace (LOAD / ADD / SUB / MOV / PRINT, no control flow)
• Per-row: full Program (instruction list) + ExecutionTrace (per-step register snapshots, output stream, halted flag) + direct-mode pre-rendered prompt (token IDs and surface text)
• Deterministic: every row is reconstructable from (meta.seed, meta.axes) via tinyvm.data.configs.TIER1.build
• Byte-integrity: every file's SHA-256 is in manifest.json (raw layout); python -m tinyvm.data verify re-hashes against it
• Seed base: 0

Two access paths

1. Native HF datasets (Parquet)

from datasets import load_dataset

ds = load_dataset("Genesis-AI-Labs/tinyvm-tier1")
ds["train"]          # 200,000 rows
ds["eval_len_16"]    # 20,000 rows (one per length bucket)

# Each row has: meta, program, trace, renders
row = ds["train"][0]
print(row["meta"]["seed"], row["meta"]["axes"])
print(row["renders"]["direct"]["input_text"])    # surface tokens
print(row["renders"]["direct"]["input_ids"])     # 64-vocab token IDs

2. Raw JSONL + manifest (byte-integrity)

The exact files emitted by python -m tinyvm.data emit --tier tier1 --out data/ are mirrored under raw/ on the Hub. Download and verify:

huggingface-cli download Genesis-AI-Labs/tinyvm-tier1 \
    --repo-type dataset \
    --include "raw/*" \
    --local-dir ./data

python -m tinyvm.data verify --dataset ./data/raw    # exits 0 if SHA-256 matches manifest

Then stream with the project's loader (skip Program/Trace reconstruction for fast DataLoader pipelines):

from tinyvm.data import load_jsonl, load_prompts

# Full row with reconstructed Program + ExecutionTrace.
for row in load_jsonl("data/raw/train.jsonl"):
    ...

# Fast path: just (input_ids, target_ids) tuples for the chosen render mode.
for input_ids, target_ids in load_prompts("data/raw/train.jsonl", mode="direct"):
    ...

Schema

Each row is a JSON object with four top-level keys:

Key	Type	Description
meta	object	{tier: "tier1", split: "train"|"eval", bucket: <name>|null, seed: int, axes: {n: int, k: int}, renders: ["direct"]}
program	array	List of instruction dicts: {op: "LOAD"|"ADD"|..., args: [...], label?: str, target?: str}
trace	object	{steps: [{pc: int, regs: [int×8]}, ...], output: [int, ...], halted: bool}
renders	object	{direct: {input_ids: [int], target_ids: [int], input_text: str, target_text: str}}

The vocabulary is the project's custom 64-token vocabulary (see tinyvm.tokeniser). Token 0 is <pad>, registers are R0..R7, etc.

Tier 1 axes

Axis	Range / Values	Meaning
n	uniformly sampled from [8, 32] (train); fixed per eval bucket	Program length in instructions
k	uniformly chosen from {2, 4, 8}	Number of distinct registers used

Eval buckets are length-stratified at n ∈ {8, 16, 32, 48, 64, 96, 128} with k=4 fixed. The len_48 through len_128 buckets test length generalisation beyond the training distribution (which tops out at n=32) — the len_128 bucket is 4× the longest training program.

Reproducibility

The dataset is bit-exact reproducible:

git clone https://github.com/mr-siddy/FANC
cd FANC
pip install -e .
python -m tinyvm.data emit --tier tier1 --out data/ --seed 0
python -m tinyvm.data verify --dataset data/tier1   # exit 0

The verify step re-hashes every emitted file's SHA-256 and compares against manifest.json. Manifest also records tinyvm_commit (git SHA at emit time) so consumers can pin to the exact code that produced the data.

Provenance

• Project: FANC — "Latent State as Computer" (research)
• Code: github.com/mr-siddy/FANC — tinyvm/data/ sub-package
• Spec: docs/superpowers/specs/2026-05-16-tinyvm-data-pipeline-design.md
• Parent doc: Latent_State_as_Computer.docx §11.2 (dataset sizes), §17 (Day 3)
• Companion datasets (to come): tinyvm-tier0 (counter programs), tinyvm-tier2 (branched programs with CoT renders)

License

MIT. Use freely.