SmolDataEnvs: rename, new README, banner

README.md

@@ -3,6 +3,7 @@ license: mit
 task_categories:
 - other
 tags:
+- smoldataenvs
 - rl-environment
 - agent
 - data-analysis
@@ -12,54 +13,85 @@ tags:
 - openenv
 ---
 
-[![View tasks in Harbor Visualiser](https://img.shields.io/badge/%F0%9F%A4%97%20Harbor%20Visualiser-View%20tasks-FFD21F?style=for-the-badge)](https://huggingface.co/spaces/HuggingFaceH4/harbor-visualiser?dataset=FineEnvs/data-agent-harbor-train)
-# 📊 Data Agent — Harbor (train)
+<div align="center">
 
-Teach an agent to *actually do data science*. This is a suite of **5,000 hands-on
-data-analysis tasks**: each one drops your agent into a sandbox with a real dataset and a
-question, and asks it to explore the data, compute the answer, and write it down. Every answer is
-checked **deterministically — no LLM judge, no guesswork**.
+<img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train/resolve/main/banner.png" alt="SmolDataEnvs" width="100%">
 
-It's packaged in [**Harbor**](https://github.com/huggingface/OpenEnv) format, so it runs as a
-ready-made agentic environment.
+# 📊 SmolDataEnvs — Harbor (train)
 
-## Where it comes from
-Built from the [**jupyter-agent dataset**](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
-— real data-science notebooks over Kaggle datasets. We extracted each question–answer pair and
-then **verified every task**: strong agent models solve it in a live sandbox and must reproduce
-the gold answer under deterministic grading. Tasks that couldn't be verified cleanly (ambiguous
-or un-checkable answers) were dropped. So **every task here is known-solvable and unambiguously
-gradable.**
+**5.5K+ RL tasks for hill-climbing small models in code and data science.**
+
+[![Collection](https://img.shields.io/badge/%F0%9F%A4%97%20Collection-SmolDataEnvs-FFD21E?style=for-the-badge&labelColor=1a1a1a)](https://huggingface.co/collections/FineEnvs/smoldataenvs)
+[![Harbor Visualiser](https://img.shields.io/badge/%F0%9F%A4%97%20Harbor%20Visualiser-Browse%20tasks-FFD21E?style=for-the-badge&labelColor=1a1a1a)](https://huggingface.co/spaces/HuggingFaceH4/harbor-visualiser?dataset=FineEnvs/SmolDataEnvs-harbor-train)
+
+</div>
+
+
+The training suite: **5,000 hands-on data-analysis tasks**. Each one drops an agent into a sandbox with a
+real dataset and a question, and asks it to explore the data, compute the answer, and write it down.
+Every answer is checked deterministically.
+
+
+Packaged in [Harbor](https://github.com/huggingface/OpenEnv) format, so each task is a ready-made
+agentic environment: its own container, its own data, its own verifier.
 
 ## What's inside
-- **5,000 verified tasks**
-- **Difficulty** — easy **1,433** · medium **2,845** · hard **722**  (`difficulty_tier`; also `difficulty_level` 1–4)
-- **Answer types** — numeric 2,906 · short-label 1,409 · list 367 · flexible 152 · yes/no 127 · csv-list 39
+
+- **5,000 verified tasks** — the RL training set
+- **Difficulty** — easy **1,433** · medium **2,845** · hard **722** (`difficulty_tier`, plus `difficulty_level` 1–5)
 
 ## How a task is laid out
+
 ```
 tasks/<task_id>/
-  task.toml         # metadata + the question, gold answer, and grading tolerances
+  task.toml         # metadata, the question, the gold answer, grading tolerances
   instruction.md    # the prompt the agent sees
-  environment/      # Dockerfile (shared base image) + data-pull hook
+  environment/      # Dockerfile (shared base image) + the data-pull hook
   tests/            # grader.py (deterministic) + test.sh
-registry.json       # index of every task
-manifest.parquet    # the same metadata as a flat table
+registry.json       # the suite manifest
+manifest.parquet    # one row per task, for filtering without walking the tree
 ```
-The dataset's CSV/SQLite files are pulled into `/home/user/input/` when the task starts.
 
-## How grading works
-The agent writes its final answer to `/workdir/answer.txt`. `grader.py` then scores it through a
-ladder of deterministic checks — **exact match → numeric tolerance → list/percent normalization →
-symbolic (math-verify)** — and returns `1.0` (correct) or `0.0`. No network, no model calls.
+## Serve it
 
-## Run it
 ```bash
-# see what resolves and how many tasks load
-openenv harbor info --dataset HuggingEnvs/data-agent-harbor-train
+openenv harbor serve \
+  --dataset FineEnvs/SmolDataEnvs-harbor-train \
+  --llm-url http://127.0.0.1:8000/v1 --model <your-model> \
+  --port 8000 --capture-port 8100
+```
 
-# run your agent/model against the suite
-openenv harbor run  --dataset HuggingEnvs/data-agent-harbor-train --model <your-model>
+Pass several with `--dataset a,b` and each arrives as its own split, which is how you train against
+`-train` and validate against `-eval` from one server.
+
+## One rollout, no trainer
+
+```bash
+openenv harbor rollout \
+  --dataset FineEnvs/SmolDataEnvs-harbor-train \
+  --llm-url http://127.0.0.1:8000/v1 --model <your-model> \
+  --harness opencode --sandbox e2b --task-index 0
 ```
-Each task gives the agent one shell/code tool, so **any tool-calling model works**, and grading
-is completely model-agnostic and offline.
+
+## Where it comes from
+
+Built from the [jupyter-agent dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
+— real data-science notebooks over 471 Kaggle datasets. Every question–answer pair was extracted and
+then **verified**: strong agent models had to solve the task in a live sandbox and reproduce the gold
+answer under deterministic grading. Anything ambiguous or un-checkable was dropped. So every task
+here is known-solvable and unambiguously gradable.
+
+**Verified by a checker, not judged by a model.** Grading is an exact comparison against a known
+answer, through a ladder of checks: exact match → numeric with tolerances → list and percent
+normalisation → symbolic equivalence. No LLM sits in the reward path, so the signal does not drift
+when you change the grader's model, because there isn't one.
+
+## The family
+
+| Repo | What it is |
+|---|---|
+| [`SmolDataEnvs`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs) | the tasks as plain rows — load it and prompt any model |
+| [`SmolDataEnvs-sft`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft) | 4,677 verified agent trajectories, TRL-ready |
+| [`SmolDataEnvs-harbor-train`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train) | 5,000 tasks as Harbor environments |
+| [`SmolDataEnvs-harbor-test`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-test) | 250 held-out, deliberately harder |
+| [`SmolDataEnvs-harbor-eval`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-eval) | 144 for quick validation during a run |

registry.json

@@ -1,6 +1,6 @@
 [
   {
-    "name": "data-agent-harbor-train",
+    "name": "smoldataenvs-harbor-train",
     "version": "2.0",
     "description": "5000 deterministic data-analysis tasks (no LLM judge).",
     "tasks": [
@@ -20006,4 +20006,4 @@
       }
     ]
   }
-]
+]

tasks/0000_324_324276_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_324_324276_qa_3"
+name = "smoldataenvs-train/0000_324_324276_qa_3"
 description = "What is the most common job role interest among new coders?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Web Development"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0000_369_369503_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-eval-v1/0000_369_369503_qa_1"
+name = "smoldataenvs-train/0000_369_369503_qa_1"
 description = "What percentage of all matches have a goal difference of zero (i.e., draws)?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "25.4%"
 reward_mode_initial = "flexible"
 package_tier = 3
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_455_455459_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_455_455459_qa_4"
+name = "smoldataenvs-train/0000_455_455459_qa_4"
 description = "What is the error rate (as a percentage) for non-legendary Pokémon in the logistic regression model's predictions?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "2"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0000_465_465850_qa_5/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_465_465850_qa_5"
+name = "smoldataenvs-train/0000_465_465850_qa_5"
 description = "Which species exhibits the highest average sepal length according to the aggregated dataset statistics?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "virginica"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_526_526258_qa_2/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_526_526258_qa_2"
+name = "smoldataenvs-train/0000_526_526258_qa_2"
 description = "How many entries are categorized into the non-NA group, edge-NA group, and interrupted group based on missing value patterns?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "141205, 32568, 4824"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0000_539_539873_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0000_539_539873_qa_3"
+name = "smoldataenvs-train/0000_539_539873_qa_3"
 description = "Which city has the lowest crime ratio, and what is the value of this ratio?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Imperial3, 0.003403"
 reward_mode_initial = "list"
 package_tier = 0
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_582_582934_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-eval-v1/0000_582_582934_qa_4"
+name = "smoldataenvs-train/0000_582_582934_qa_4"
 description = "Which state has the lowest proportion of shootings involving individuals with signs of mental illness?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Kentucky (KY)"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_587_587336_qa_5/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_587_587336_qa_5"
+name = "smoldataenvs-train/0000_587_587336_qa_5"
 description = "What is the percentage of total gun-related shootings in Washington state attributed to individuals with mental illness compared to those without?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "45%"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_641_641256_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_641_641256_qa_1"
+name = "smoldataenvs-train/0000_641_641256_qa_1"
 description = "Which state has the highest average effective literacy rate, and what is that rate?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Mizoram, 98.8"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_656_656399_qa_2/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0000_656_656399_qa_2"
+name = "smoldataenvs-train/0000_656_656399_qa_2"
 description = "Which pair of numerical features in the dataset shows the strongest positive correlation according to the correlation analysis?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "raisedhands, VisITedResources"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_767_767688_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0000_767_767688_qa_4"
+name = "smoldataenvs-train/0000_767_767688_qa_4"
 description = "According to the scatter matrix analysis, which three features exhibited the highest linear correlation with each other in the dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "perimeter_mean, area_mean, radius_mean"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_780_780974_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0000_780_780974_qa_4"
+name = "smoldataenvs-train/0000_780_780974_qa_4"
 description = "What was the maximum number of arrests recorded at the Southwest border and in which year?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "1643679 in 2000"
 reward_mode_initial = "list"
 package_tier = 0
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_804_804467_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0000_804_804467_qa_1"
+name = "smoldataenvs-train/0000_804_804467_qa_1"
 description = "Which model achieved the highest accuracy using KFold cross-validation, and what was the accuracy score?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "RandomForest, 1.0"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0000_804_804467_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0000_804_804467_qa_3"
+name = "smoldataenvs-train/0000_804_804467_qa_3"
 description = "What is the lowest RMSE value observed in the train_test_split results, and which models achieved it?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "0, DecisionTree, RandomForest, SVM"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0000_806_806826_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0000_806_806826_qa_3"
+name = "smoldataenvs-train/0000_806_806826_qa_3"
 description = "How does the number of years with above-average temperature changes from February to March compare to the number of years with below-average changes in the dataset spanning 1895-2016?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "62 above, 60 below"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_849_849952_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_849_849952_qa_4"
+name = "smoldataenvs-train/0000_849_849952_qa_4"
 description = "Which group of Western European countries exhibited synchronized fluctuations in Christian adherents over the five decades of analysis?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Western European countries"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0000_886_886039_qa_2/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_886_886039_qa_2"
+name = "smoldataenvs-train/0000_886_886039_qa_2"
 description = "Which defender was defeated the most times in the dataset, and how many times were they defeated?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Robb Stark, 13"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_981_981197_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0000_981_981197_qa_1"
+name = "smoldataenvs-train/0000_981_981197_qa_1"
 description = "Which four features were identified as the top-performing predictors for diabetes classification using the chi-square ($\\chi^2$) feature selection method?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Glucose, Insulin, BMI, Age"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0000_982_982280_qa_2/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_982_982280_qa_2"
+name = "smoldataenvs-train/0000_982_982280_qa_2"
 description = "What is the item with the highest Trans Fat content, and what is its Trans Fat value in grams?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Double Quarter Pounder with Cheese, 2.5"
 reward_mode_initial = "list"
 package_tier = 0
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0000_992_992184_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0000_992_992184_qa_3"
+name = "smoldataenvs-train/0000_992_992184_qa_3"
 description = "What are the keys present in the loaded PETCT dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "ct_data, label_data, pet_data"
 reward_mode_initial = "list"
 package_tier = 3
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0001_042_1042725_qa_5/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0001_042_1042725_qa_5"
+name = "smoldataenvs-train/0001_042_1042725_qa_5"
 description = "Which two countries have the most top 100 male marathon runners after the USA in the dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Kenya, Ethiopia"
 reward_mode_initial = "list"
 package_tier = 3
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_074_1074738_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_074_1074738_qa_1"
+name = "smoldataenvs-train/0001_074_1074738_qa_1"
 description = "Which U.S. state has the highest number of recorded \"Murder or Manslaughter\" cases, and what is the exact count of such incidents in that state?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "California, 98994"
 reward_mode_initial = "list"
 package_tier = 3
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_074_1074738_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0001_074_1074738_qa_4"
+name = "smoldataenvs-train/0001_074_1074738_qa_4"
 description = "Which relationship category between victims and perpetrators is most prevalent in the dataset, and what percentage of homicide cases fall into this category?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Unknown, 42.76%"
 reward_mode_initial = "list"
 package_tier = 3
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_085_1085629_qa_2/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-eval-v1/0001_085_1085629_qa_2"
+name = "smoldataenvs-train/0001_085_1085629_qa_2"
 description = "What are the top three features according to the feature importance ranking provided by the Extra Trees Classifier?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "fnlwgt, age, hours.per.week"
 reward_mode_initial = "list"
 package_tier = 0
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0001_085_1085629_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0001_085_1085629_qa_4"
+name = "smoldataenvs-train/0001_085_1085629_qa_4"
 description = "What is the accuracy of the KNN model when using only the top two features from the Feature Importance ranking (fnlwgt and age) with the optimal K value?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "0.762"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0001_090_1090499_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0001_090_1090499_qa_1"
+name = "smoldataenvs-train/0001_090_1090499_qa_1"
 description = "Which two nationalities have the highest representation in the dataset based on the analysis?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Kuwait, Jordan"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0001_133_1133625_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_133_1133625_qa_4"
+name = "smoldataenvs-train/0001_133_1133625_qa_4"
 description = "In which phase did BJP+ achieve the highest percentage of total votes, and what was that percentage?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Phase 1, 45.48"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 3
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_137_1137361_qa_2/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0001_137_1137361_qa_2"
+name = "smoldataenvs-train/0001_137_1137361_qa_2"
 description = "What is the total number of check-ins recorded in the New York City dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "227428"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0001_137_1137537_qa_2/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0001_137_1137537_qa_2"
+name = "smoldataenvs-train/0001_137_1137537_qa_2"
 description = "What are the geographic coordinates of the closest check-in point to the convex hull centroid in New York City?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "40.77607305, -73.98191214"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0001_155_1155051_qa_5/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_155_1155051_qa_5"
+name = "smoldataenvs-train/0001_155_1155051_qa_5"
 description = "What is the average age for customers who defaulted compared to those who did not?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Defaulters: 35.73, Non-defaulters: 35.42"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_155_1155264_qa_5/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-eval-v1/0001_155_1155264_qa_5"
+name = "smoldataenvs-train/0001_155_1155264_qa_5"
 description = "What is the most common instance type in the south zone identified through the analysis?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "m4.large"
 reward_mode_initial = "exact_short"
 package_tier = 0
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_160_1160639_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_160_1160639_qa_1"
+name = "smoldataenvs-train/0001_160_1160639_qa_1"
 description = "What is the average opening price of Nifty 50 across all recorded dates in the dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "7374.52"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0001_170_1170198_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0001_170_1170198_qa_3"
+name = "smoldataenvs-train/0001_170_1170198_qa_3"
 description = "After imputing missing values with the column mean, how many missing values remain in the dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "0"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_170_1170198_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_170_1170198_qa_4"
+name = "smoldataenvs-train/0001_170_1170198_qa_4"
 description = "What is the frequency of the most populated bin in the 'huml' histogram, and what is the bin range?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "234, 9.85 to 50.865"
 reward_mode_initial = "list"
 package_tier = 1
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_173_1173665_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_173_1173665_qa_3"
+name = "smoldataenvs-train/0001_173_1173665_qa_3"
 description = "After normalization, what is the mean value of the 'Balance' feature?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "0.3048"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_173_1173665_qa_5/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_173_1173665_qa_5"
+name = "smoldataenvs-train/0001_173_1173665_qa_5"
 description = "Which feature, 'Balance' or 'EstimatedSalary', has a higher standard deviation after normalization?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "EstimatedSalary"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_175_1175291_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_175_1175291_qa_4"
+name = "smoldataenvs-train/0001_175_1175291_qa_4"
 description = "What is the maximum earthquake magnitude recorded in the dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "9.1"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0001_181_1181828_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0001_181_1181828_qa_4"
+name = "smoldataenvs-train/0001_181_1181828_qa_4"
 description = "Which model demonstrated the highest training accuracy but the lowest test accuracy in the comparison analysis?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Decision Tree"
 reward_mode_initial = "exact_short"
 package_tier = 2
 difficulty_level = 4
+difficulty = "hard"
 difficulty_tier = "hard"
 
 [environment]

tasks/0001_182_1182948_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_182_1182948_qa_1"
+name = "smoldataenvs-train/0001_182_1182948_qa_1"
 description = "What is the minimum recorded solar radiation value in the dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "1.11"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0001_188_1188925_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0001_188_1188925_qa_1"
+name = "smoldataenvs-train/0001_188_1188925_qa_1"
 description = "What is the mean age of individuals who defaulted on their credit card payments compared to those who did not?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Default: 35.73, Non-Default: 35.42"
 reward_mode_initial = "list"
 package_tier = 2
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_188_1188925_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify/0001_188_1188925_qa_3"
+name = "smoldataenvs-train/0001_188_1188925_qa_3"
 description = "What percentage of the dataset consists of credit card defaults?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "22"
 reward_mode_initial = "numeric"
 package_tier = 2
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]

tasks/0001_189_1189227_qa_1/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0001_189_1189227_qa_1"
+name = "smoldataenvs-train/0001_189_1189227_qa_1"
 description = "What percentage of the dataset represents credit card defaults?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "22"
 reward_mode_initial = "numeric"
 package_tier = 2
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_189_1189227_qa_2/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_189_1189227_qa_2"
+name = "smoldataenvs-train/0001_189_1189227_qa_2"
 description = "What is the mean age of credit card holders who defaulted?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "35.73"
 reward_mode_initial = "numeric"
 package_tier = 2
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_189_1189227_qa_5/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0001_189_1189227_qa_5"
+name = "smoldataenvs-train/0001_189_1189227_qa_5"
 description = "How many samples were allocated to the training set?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "24000"
 reward_mode_initial = "numeric"
 package_tier = 2
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_191_1191057_qa_4/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0001_191_1191057_qa_4"
+name = "smoldataenvs-train/0001_191_1191057_qa_4"
 description = "Which original features were removed from the dataset because they contained only a single unique value across all observations?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "EmployeeCount, Over18, StandardHours"
 reward_mode_initial = "list"
 package_tier = 2
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_193_1193343_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "train-verify2/0001_193_1193343_qa_3"
+name = "smoldataenvs-train/0001_193_1193343_qa_3"
 description = "Which cuisine type is mentioned most frequently in the \"fav_cuisine\" column of the dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "italian"
 reward_mode_initial = "exact_short"
 package_tier = 3
 difficulty_level = 2
+difficulty = "medium"
 difficulty_tier = "medium"
 
 [environment]

tasks/0001_196_1196803_qa_3/task.toml

@@ -2,10 +2,10 @@ schema_version = "1.2"
 artifacts = []
 
 [task]
-name = "data-agent-train-v1/0001_196_1196803_qa_3"
+name = "smoldataenvs-train/0001_196_1196803_qa_3"
 description = "What is the most common ownership type among all Starbucks stores in the dataset?"
 authors = []
-keywords = ["data-agent", "data-analysis", "kaggle"]
+keywords = ["smoldataenvs", "data-analysis", "kaggle"]
 
 [metadata]
 source_dataset = "jupyter-agent/jupyter-agent-dataset"
@@ -15,6 +15,7 @@ gold_answer = "Company Owned"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 1
+difficulty = "easy"
 difficulty_tier = "easy"
 
 [environment]