File size: 7,568 Bytes
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> **Training split only.** This repository publishes the `dev_task_names` half
> of each pinned split. The held-out/test tasks are not included, and neither
> are the published splits that are not the pin. Every split file here is
> reproduced byte-for-byte from the source repository, so `assignment_sha256`
> still verifies and `heldout_task_names` still names the absent test tasks.
One folder per dataset. The file inside is the pinned resolved train/test
assignment over the tasks of that dataset, published without its private random
seed.
The tasks themselves stay in the dataset folder at the top level of this
repository, so the `dev_task_names` listed in
`splits/tau3-bench/tau3-banking-70-s001.json` are directories under
`tau3-bench/tasks/`. The `heldout_task_names` are not.
```
splits/
QuantitativeFinance-Bench/
healthbench/
hle/
tau3-bench/
```
## Which split we use
Several datasets carry more than one published split. Exactly one of them is
the pin: the split that the skilltrainbench launch skills hardcode and that the
reported results were produced on. Only the pin is published here; the others
stay in the source repository, for replication and for pipeline tests, and a
number produced on them is not comparable to a number produced on the pin.
| Dataset | Pinned split | Task set | Dev / train | Held-out / test | Learner pool | Claude Code `max_turns` | OpenHands `max_iterations` |
|---|---|---|---:|---:|---:|---:|---:|
| QuantitativeFinance-Bench | `qf-medhard-strat-80-s005` | `medium_hard` | 54 | 14 | 160M | 41 | 45 |
| healthbench | `hb-hardbeh-s002` | `hard_behavioural` | 200 | 160 | 80M | 11 | 12 |
| hle | `hle-stem517-s001` | `stem517` | 412 | 105 | 400M | 36 | 40 |
| tau3-bench | `tau3-banking-70-s001` | `banking_knowledge` | 67 | 30 | 360M | 51 | 60 |
**The four benchmark pools sum to 1,000M and that total is a hard limit.** Each
pool is `dev x 4 x` the mean weighted tokens (input + 5 x output) of a
within-cutoff GLM 5.3 Flash attempt, the four scaled together to sum to 1B,
which is what a two-person hackathon team's $200 of GLM buys. The mean counts an
over-cutoff attempt with the tokens it had reached at the cut rather than
dropping it.
The learner pool is the `--budget-tokens` pool for skill formation. It caps the
learner only. Curator spend, judge spend and simulator spend are metered in
their own pools and are never charged against it.
The tool-call cutoff is one number per benchmark, applied identically to every
learner. Claude Code `max_turns` counts model responses and OpenHands
`max_iterations` counts loop steps, so neither is an exact tool-call ceiling;
the numbers above are the serial-call allowance, with OpenHands given about 20%
more. **A cutoff hit is a failed attempt**, and its rate is reported per arm
next to the lift.
The published results predate this table. They were measured at 100M
(QuantitativeFinance-Bench), 100M (healthbench), 300M (tau3-bench) and 500M
(hle), with healthbench at 4 turns and the others effectively uncapped. Those
runs stay valid as published, but a new run and an old run are not on the same
budget. Rescoring an old run under the new cutoffs is not valid either: the
probe signal, and so the skill, would have differed.
Each pinned split verifies against these digests:
| Dataset | `assignment_sha256` |
|---|---|
| QuantitativeFinance-Bench | `f256dfa217a335f88a925c088b78289553de0a9cadaec96153d682cabf60ec0f` |
| healthbench | `32b8ab7615506a55e9fa3ca89ee1d6e68f66a771f62636f2876d2ec99ca68cfb` |
| tau3-bench | `90d6bcc7f58b181725dc4c0f21960b1459c6b9bacdb6ef09f2fb7eda30fb27b6` |
| hle | `3b1755f035bf7135bfdd4b40bf376cb157cc7f7a49739e48da92e35be06d5aeb` |
## Pins that are not splits
A run is only reproducible if the environment is pinned too. These values live
in `src/skilltrainbench/harbor_policy.py` in the skilltrainbench repository and
are applied when a task tree is staged, so a task Dockerfile in this repository
may name an unpinned tag while the run that used it did not.
| Dataset | Base image | Upstream code pinned at build time |
|---|---|---|
| QuantitativeFinance-Bench | `doenermannti/finance-bench-sandbox@sha256:a6766c8b32cae3425feaddb8d9bac185de866323307290b7c74aed30f9c928ef` | none |
| healthbench | `python:3.12-slim@sha256:2fe5997d249a808b8eeea52c58a1dbffbba28754dc11699ef5c029f2d818ce79` | `openai/simple-evals@496e4570446e9f0499a00557a231a440c446df76` |
| tau3-bench | `python:3.12-slim@sha256:2fe5997d249a808b8eeea52c58a1dbffbba28754dc11699ef5c029f2d818ce79` | `sierra-research/tau2-bench@672227c6b6` |
| hle | `python:3.11-slim@sha256:9534e5a8e315485d4061ed659af0fd78a284c015f9b73661b41d6bab25604534` | `harbor-framework/harbor-datasets@37db108843a49bb31a592e37a75e2c40dc3f9749` |
The tau3-bench images clone `tau2-bench` while they build, so that clone has to
be pinned or the image silently tracks upstream. Override it with
`STBENCH_TAU3BENCH_TAU2_COMMIT` only when you mean to.
### Grader and simulator models
These are configuration, not dataset content, and a run records what it used.
- healthbench. The grader is the pinned `simple-evals` HealthBench grader,
vendored as `healthbench_eval.py` with SHA-256
`b763d8c1f53ecd165f0d170a32bc948216b3dfeadafe238b2083328e1af4b2df`. The
official judge configuration for the benchmark is `gpt-4.1-2025-04-14` at
temperature `0.5`. The reported `hb-hardbeh-s002` run deliberately deviates
and used `claude-haiku-4-5-20251001` at temperature `0`, with its own 40M
judge pool. Say which one you used.
- tau3-bench. The simulated user and the natural-language assertion judge run
through `TAU2_USER_MODEL` and `TAU2_NL_ASSERTIONS_MODEL`, both
`openai/claude-sonnet-4-6` with `TAU2_USER_REASONING_EFFORT=low` in the
reported run. They are metered in a separate simulator pool.
## Selecting a split
```bash
--benchmark tau3bench --task-set banking_knowledge --split-id tau3-banking-70-s001
```
`dev_task_names` is the training set and `heldout_task_names` is the test set.
Split identifiers are immutable. If a task set definition or the task bytes
change, create a new identifier rather than reusing one; the loader fails
closed when `task_set_definition_sha256` or `selected_source_snapshot_sha256`
no longer matches.
## Fields
- `schema_version`: split artifact schema.
- `split_id`: immutable identifier for this assignment.
- `benchmark`: skilltrainbench benchmark adapter.
- `task_set`: selected task-set identifier.
- `train_ratio_in_split`: requested train proportion.
- `stratified`: whether the assignment was stratified.
- `dev_task_names`: development names; treat these as train tasks.
- `heldout_task_names`: held-out names; treat these as test tasks.
- `counts`: resolved development and held-out counts.
- `strata`: counts per stratum, where the assignment was stratified.
- `task_set_definition_sha256`: digest of the task-set definition.
- `selected_source_snapshot_sha256`: digest of the selected task bytes used to
resolve this assignment.
- `assignment_sha256`: digest of the public, seed-stripped assignment.
The private random seed is removed from every file published here. It stays in
the skilltrainbench repository under the ignored `private/splits/` directory.
## Download
```bash
hf download armin-aptura/skilltrainbench-public --repo-type dataset \
--include "splits/**" --local-dir .
```
For one dataset and its split together:
```bash
hf download armin-aptura/skilltrainbench-public --repo-type dataset \
--include "tau3-bench/**" --include "splits/tau3-bench/**" --local-dir .
```
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