dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: ground_truth
dtype: string
- name: dataset
dtype: string
- name: env_config
struct:
- name: env_name
dtype: string
- name: image
dtype: string
- name: task_id
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 133363912
num_examples: 37484
download_size: 29005927
dataset_size: 133363912
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
language:
- en
tags:
- tmax
- terminal
- agents
- reinforcement-learning
Recursive-Task-Synthesis for tmax
Build images locally: the complete dataset and build contexts are retained. The hosted SIF image cache has been retired; see the rebuilding instructions below.
All 37,484 task directories from Zhongzhi1228/Recursive-Task-Synthesis, pinned to be44f96808d5a9b599d5cb024341ff00091adeb7, converted to tmax's swerl_vanillux_sandbox format.
The train split uses the same messages, ground_truth, dataset, env_config, and source schema as the other hamishivi/agent-task-* datasets. Messages use the tmax Vanillux templates; dataset is passthrough. Task IDs are prefixed with recursive_task_synthesis__ to avoid collisions when combining task archives. task-manifest.json maps them back to original paths.
task-data.tar.gz contains a directory per task with all original task files preserved byte-for-byte, plus image.txt. Instructions, verifiers, build contexts, metadata, and reference solutions are retained. The tmax loader exposes only environment seed files and defers tests until submission; reference solutions are not mounted into the agent sandbox.
Usage
from datasets import load_dataset
from huggingface_hub import hf_hub_download
train = load_dataset("hamishivi/agent-task-recursive-task-synthesis", split="train")
archive = hf_hub_download("hamishivi/agent-task-recursive-task-synthesis", "task-data.tar.gz", repo_type="dataset")
Extract the archive and set the sandbox task_data_dir to the directory containing the task directories. Every row specifies its image explicitly. Task resource requirements and verifier timeouts remain in the original task.toml; configure the training harness accordingly.
Images
Images use hamishi740/agent-task-recursive-task-synthesis:<source-environment-hash> and target Linux AMD64. See task-manifest.json for build status.
Validation and attribution
All rows passed schema and Parquet round-trip checks, and every archived task file was checked against the downloaded source bytes. See validation.json. This does not constitute an evaluation of every task or a full training run.
The upstream license is cc-by-4.0. Consult the upstream dataset card for citation and provenance. tmax Vanillux prompts are adapted from mini-swe-agent (MIT).
Reproducibility and runtime coverage
This conversion targets hamishivi/tmax-private, branch geomean_mask, commit 9fff7af6e86f2589830637772ff5b10b1c080131. All original task files are preserved; added task IDs, training rows, and image references adapt the packaging for tmax. See UPSTREAM_README.md for the source authors' attribution and citation details.
Validation covers all task records and archived file contents. No container build or runtime task evaluation was performed for this release. Upstream validation claims do not replace testing in your training harness.
Resumable image builds
Requires Docker Buildx and a Docker Hub login with write access to the target image repository. The script skips published tags and digest-pinned upstream images, saves per-image logs, and retries unfinished builds on the next run.
mkdir -p tasks
tar -xzf task-data.tar.gz -C tasks
python build_dataset_images.py --task-data-dir tasks --manifest task-manifest.json --workers 8 --timeout 1800
Images built by this script target Linux AMD64. If using another Docker Hub namespace, update image references consistently in the manifest, training records, and task image.txt files. The image status file is a publication-time snapshot.
The source includes 67 references to three unavailable local image names. These tasks use the supplied build contexts and new image targets; the original names are retained in the manifest as unavailable_upstream_image. Source task-group IDs are also retained for grouping and splitting related tasks.
Hosted image pool retired
The hosted Apptainer SIF cache was removed on 2026-10-19 to reduce repository storage. All 37,484 task records, original task files, verifiers, build contexts, and attribution remain available in this dataset. Prebuilt SIF downloads and the old image download revisions are no longer available.
The original apptainer/manifest.jsonl files are preserved byte-for-byte as historical build records. The complete 29,501-image build provenance, including Docker runtime configuration and recorded repairs, is in the TMaxxx manifest. Their status, hf_path, and commit fields describe the retired publication and are no longer active download locations. apptainer/status.json marks the pool as retired and every environment as requiring a local build.
Rebuild images locally
Download task-data.tar.gz, task-manifest.json, and build_dataset_images.py from this dataset, then run:
mkdir -p tasks
tar -xzf task-data.tar.gz -C tasks
python build_dataset_images.py --task-data-dir tasks --manifest task-manifest.json \
--load --max-images 1 --log-dir local-image-build-logs
--load builds Linux AMD64 images into your local Docker engine without a Docker Hub login. Start with one image; omit --max-images 1 when ready to build all environments. The existing push behavior remains available by omitting --load and using a registry account with permission to push the requested image names.
The original Dockerfiles may need the recorded build compatibility repairs in context_repair, such as archived Debian packages or CentOS mirrors. Rebuilding does not guarantee the exact bytes of the retired SIFs. Task fixtures and verifiers remain unchanged.
To use a locally built Docker image with Apptainer, use image and sif_name from the environment manifest:
mkdir -p sifs
docker save "$IMAGE" -o image.tar
apptainer build "./sifs/$SIF_NAME" "docker-archive://$PWD/image.tar"
export SWERL_APPTAINER_SIF_DIR="$PWD/sifs"