Eve39570/verified-analytics-tasks

Verified Analytics Tasks

150+ mainly small analytics and data-engineering tasks. The point of the set is the answer key: every task ships its own automated checker, and every gold answer was run through that checker and scored a clean 1.0 before the task was allowed in. So the labels are more like "here's the checker, score it yourself" instead of "just trust me bro."

I wanted to create a synthetic dataset inspired by this paper: Autodata: An agentic data scientist to create high quality synthetic data, while trying to be very spend-savvy. Deterministic Python computes both the fixture and the gold answer from the same seed, so there are no frontier-model fingerprints on the labels, and nothing to relitigate when someone's terms of service change on a Tuesday cough cough.

What's inside

Each task is a prompt over a seeded CSV or SQLite fixture, drawn from 15 checker templates across three families:

• Analytics: grouped sums/counts/means, filtered totals, top-N, dedupe-then-aggregate, date windows, pivots, joins, threshold flags. Answers are JSON values, checked by value with float tolerance (math.isclose, never ==).
• Regex: the answer is a pattern checked by what it extracts from held-out test strings, never by comparing pattern text.
• SQL: the answer is a query, checked by executing it and the reference query against the same fixture and comparing result rows.

Every template embeds a deliberate trap where the specific plausible-but-wrong answer it's built to catch, like forgetting abs(), using > where >= is correct at a boundary, counting nulls as zero, or dropping a status='paid' filter. Each checker is unit-tested against three cases: a correct answer, the trap, and a malformed one.

The tables

The dataset compiles to seven JSONL tables, one config each in the viewer:

config	what it holds
experience	one row per task: prompt, fixture reference, template, gold answer
fixture_generators	the seeded generator spec that produces each fixture
sft_examples	prompt → gold-answer pairs, ready for supervised fine-tuning
rollouts	each gold answer replayed through its own checker (all score 1.0): a verification-pass log, not captured model output
preference_pairs	chosen (gold, 1.0) vs. rejected (a deterministic near-miss, 0.0), with the checker's rejection reason attached
verifiers	the checker for each task
rubric_items	the scoring criteria behind each checker

How the labels are produced

Every template implements the same contract:

generate(seed, params)             # writes the fixture file(s)
reference(params)                  # returns the gold answer
check(agent_answer, gold, params)  # -> {"gate_passed": bool, "score": float, "reason": str}

Checking gates first: wrong shape scores 0 with no partial credit, and only answers that clear the gate get scored. A task is admitted only if reference()'s own output scores 1.0 through check(), just to guarantee that the set is "verified" rather than "labelled."

Intended use

This is a verifiable-task suite for RL-with-verifiable-rewards (RLVR) and for gradeable evaluation, where you need a reward signal you can trust without a model or a human in the loop. It is not a distillation corpus, there are no captured agent traces here.

Limitations

Treat this as the limitations section of a paper, not fine print.

• The preference pairs are uneven in signal. For the SQL and regex templates, both sides of a pair are programs and the checker's row-set comparison is binary, so the chosen/rejected gap attempts to reflect a real behavioural difference, those are the sharp pairs. For the analytics templates, both sides are bare numeric values, and the rejected side is a deterministic perturbation of the gold number. A model can often tell those apart without understanding why one is right, so the signal is weaker. Each row is labelled with its signal level so you can filter; if you want an unimpeachable DPO seed, take the program-valued pairs and leave the rest.
• It's synthetic and narrow. Every fixture is generated, not sampled from real-world data, and the domain is analytics/data-engineering specifically. Good for a clean reward signal but still not a substitute for messy (and expensive) real inputs.
• Difficulty is bounded. These are well-posed, single-mechanism tasks. A careful solver clears them, the value is a trustworthy checker on a known mechanism, not an unsaturated difficulty frontier.
• Small by design. 151 tasks across 15 templates. Enough to be useful as an RLVR/eval seed; not a large-scale training corpus.

License

Apache-2.0, covering both the data and the checker code the tables reference.