SeanWang0027/scienceworld-tcod-split

ScienceWorld — TCOD task-type split

The ScienceWorld task list used by TCOD and FutureBridge-OPD, in the exact row format their workflows parse, with the machine-local jar path removed so the rows load anywhere.

split	rows	task types
train	2294	17
test	1308	13

The 1308-row test split is the one reported as "all 1,308 entries in the disjoint task-type test split" in FutureBridge-OPD's configs/README.md.

This is a task list, not trajectories. Each row names a ScienceWorld task type and variation; the environment generates the episode at rollout time. You need the scienceworld package (and a JVM) installed to use it.

Schema

Two columns, matching what TCOD's buffer reader expects with format.prompt_key: task_desc:

column	type	contents
task_desc	string	JSON: {"task_name": ..., "var_num": ..., "jar_path": ""}
targe	string	always ""

targe is TCOD's own spelling of "target". It is kept as-is so their loader finds the key it expects.

About jar_path: ""

Upstream's generator writes the absolute path of the local scienceworld.jar into every row, which makes the files unusable on any other machine. Here it is blanked, which is safe and portable:

• TCOD's loader does jar_path = task_config.get("jar_path", ""), then ScienceWorldEnv("", jar_path, envStepLimit=...)
• ScienceWorldEnv.__init__ does serverPath = serverPath or JAR_PATH

An empty string is falsy, so the simulator falls back to the jar shipped inside the installed scienceworld package. Nothing downstream needs patching. If you do need a specific jar, set the field yourself.

Usage

Straight from a TCOD / Trinity-RFT YAML

_load_task_dataset falls through to load_dataset(path, split=...), so the repo id works as a path — no manual download:

buffer:
  explorer_input:
    taskset:
      name: sciworld
      storage_type: file
      path: SeanWang0027/scienceworld-tcod-split
      split: train
      format:
        prompt_key: 'task_desc'
      rollout_args:
        temperature: 1.0
        logprobs: 0
      workflow_args:
        temperature: 1.0
        max_env_steps: 30
    eval_tasksets:
      - name: sciworld_eval
        storage_type: file
        path: SeanWang0027/scienceworld-tcod-split
        split: test
        total_steps: 1308
        task_selector:
          selector_type: sequential
        format:
          prompt_key: 'task_desc'
        rollout_args:
          temperature: 0.4
          logprobs: 0
    default_workflow_type: 'OPD_scienceworld_workflow'

As a plain dataset

from datasets import load_dataset
import json

ds = load_dataset("SeanWang0027/scienceworld-tcod-split")
cfg = json.loads(ds["test"][0]["task_desc"])
# {'task_name': 'find-non-living-thing', 'var_num': 85, 'jar_path': ''}

from scienceworld import ScienceWorldEnv
env = ScienceWorldEnv("", cfg["jar_path"], envStepLimit=100)
env.load(cfg["task_name"], cfg["var_num"], "easy", generateGoldPath=False)
obs, info = env.reset()

Note the third load argument: TCOD's workflow passes simplification_str="easy" by default.

How the split was made

Reproduced from TCOD_examples/scienceworld/get_sciworld_data.py, not re-implemented — build_split.py in this repo imports that script and calls its create_dataset_files with percentage=0.5 and upstream's hardcoded 17-task train list. Test task types come from a set difference, so the two splits are disjoint by construction.

For each task type, variations 0 .. int(count * 0.5) - 1 are taken.

Verification

• Row-for-row identical to the upstream generator on (task_name, var_num) and ordering; only jar_path differs.
• No task type appears in both splits (17 train / 13 test, 30 total).
• All 30 hardcoded variation counts match ScienceWorld 1.2.3's own get_variations_train/dev/test totals, so no emitted var_num is out of range.
• Sampled rows from both splits load, reset() and step() through TCOD's own _create_scienceworld_env using the package's builtin jar.

Caveats worth knowing before you report numbers

This is not ScienceWorld's official split. The official one splits variations within each of the 30 task types (env.get_variations_train/dev/test()), so every task type appears in both train and test. This one holds out whole task types. The two measure different things and are not comparable.

Many held-out types are near-siblings of trained ones. 8 of the 13 test types have a close analogue in train: measure-melting-point-known-substance → -unknown-substance, test-conductivity → test-conductivity-of-unknown-substances, inclined-plane-friction-named-surfaces → -unnamed-surfaces, mendelian-genetics-known-plant → -unknown-plant, find-living-thing/find-plant → find-non-living-thing/find-animal, lifespan-shortest-lived → lifespan-longest-lived, chemistry-mix-paint-secondary-color → -tertiary-color, power-component → power-component-renewable-vs-nonrenewable-energy. Zero-shot transfer to a new task type is a weaker claim here than the phrasing suggests.

There is no dev split. Upstream YAMLs point eval_tasksets straight at test, so anything used to pick a checkpoint is also what gets reported. Fix the reporting rule in advance (e.g. always report the final checkpoint), or carve a dev set out of train.

Test is dominated by two task types. test-conductivity-of-unknown-substances (300) and mendelian-genetics-unknown-plant (240) are 41% of the 1308 rows, while identify-life-stages-1 has 7. Rows are shuffled with seed 42, so a truncated evaluation still samples proportionally — but a plain mean over the test split is largely those two task types. Per-task-type breakdowns are in split_stats.json.

Half of every task type's variations are unused. Only indices below count * 0.5 are emitted, in both splits — 3605 variations are dropped entirely.

Reward

TCOD's ScienceWorld workflow uses best_score / 100.0, where best_score is the highest info["score"] seen at any point in the episode. ScienceWorld scores range over [-100, 100], so this reward can be negative, and a mid-episode peak is not lost by later mistakes.

License and provenance

Apache-2.0, matching TCOD and FutureBridge-OPD. The underlying environment is ScienceWorld; the split definition is from TCOD.