Datasets:
pretty_name: PhySimCode data v4
task_categories:
- video-text-to-text
tags:
- physics
- simulation
- code-generation
- video
size_categories:
- 100K<n<1M
PhySimCode data v4
Training and evaluation data for physics video → simulation code. The input is a short video of a physics simulation. The target has two parts:
- a chain of thought (CoT): the experiment name and the governing physical law;
- a self-contained Python program that re-renders the video.
The post-training code (SFT, evaluation, GRPO) is in the GitHub repo SourajaKundu/Physics-Simulation-Code-Generation, folder post_training/.
| Folder | Content | Size |
|---|---|---|
scipy_v4/ |
scipy engine, ready-made data_v4 splits for SFT, RL and evaluation | 7.3 GB |
kubric/ |
the complete kubric engine dataset, raw samples (not used for training) | 4.2 GB |
Quick start (recommended)
Clone the code repo and run its download script. It downloads into <repo>/data/, unpacks the tar shards, and rewrites the video paths. All training and evaluation scripts read from that location by default, so no paths need editing.
git clone https://github.com/SourajaKundu/Physics-Simulation-Code-Generation.git
cd Physics-Simulation-Code-Generation
pip install -U "huggingface_hub[cli]"
hf auth login # a token from an account that has been granted access to this dataset
bash post_training/data/download_data.sh # scipy_v4 only: everything SFT, eval and RL need (7.3 GB)
bash post_training/data/download_data.sh kubric # optional: the kubric dataset (4.2 GB)
Then follow post_training/README.md for the environment setup, SFT, evaluation and GRPO.
Resulting layout:
Physics-Simulation-Code-Generation/
└── data/ # $DATA_ROOT
├── scipy_v4/ # $PHYSIM_DATA
│ ├── sft_train.jsonl rl_train.jsonl val_seen.jsonl val_unseen.jsonl rl_val.jsonl
│ ├── split_manifest.csv gt_video_meta.json
│ ├── videos/scipy/<experiment>/<sample_id>.mp4 # model inputs
│ └── ref_videos/scipy/<experiment>/<sample_id>.mp4 # original videos (RL reward, PSNR)
└── kubric/ # $PHYSIM_KUBRIC
├── manifest.csv
└── dataset_kubric/<experiment>/<sample_id>/{video.mp4, params.json, cot.json, simulation_code.py}
To store the data somewhere else, set DATA_ROOT=/big/disk/physimcode before running the download script, and set it again before running any training script. post_training/env.sh reads it.
Manual download
hf download SourajaKundu123/PhySimCode-data-v4 --repo-type dataset --include "scipy_v4/*" --local-dir data
cd data/scipy_v4 && for t in *.tar; do tar -xf "$t" && rm "$t"; done && cd -
python post_training/data/relocate.py data/scipy_v4 # rewrite video paths to absolute (ms-swift needs this)
In the released jsonl files, video paths are relative to scipy_v4/, for example videos/scipy/<experiment>/<sample_id>.mp4. relocate.py turns them into absolute paths and reports any missing video. It is idempotent.
scipy_v4: the data_v4 split
Source. All 64,756 samples of the scipy engine, covering 87 experiments.
- Every sample's
params.json,cot.json,simulation_code.pyand video were audited for consistency. - Running the stored code reproduces the stored video byte-for-byte.
- The 1,305 scipy samples of the PhySimCode benchmark are excluded from every split.
Held-out experiments. 13 of the 87 experiments (15%) are held out of all training data, stratified by physics domain: block_belt_spring_stickslip, block_on_sphere_basin, cart_pendulum, colliding_blocks, complex_collision_plane, driven_string_ring, hemisphere_roll_off, ring_pendulum, stacked_balls_drop, string_block_wave, u_tube_fluid_oscillation, watt_straight_line_linkage, wedge_sphere.
| File | Rows | Use |
|---|---|---|
sft_train.jsonl |
21,398 | SFT (74 training experiments, up to 300 per experiment) |
rl_train.jsonl |
7,400 | RL (same 74 experiments, 100 per experiment, disjoint from SFT) |
val_seen.jsonl |
370 | 5 per training experiment, never trained on |
val_unseen.jsonl |
390 | 30 per held-out experiment |
rl_val.jsonl |
87 | 1 per experiment (seen and unseen), cheap RL validation |
split_manifest.csv |
64,756 | experiment, domain, sample_id, split for every scipy sample. Splits: sft, rl, val_seen, val_unseen, reserve (unused, seen experiments), holdout_reserve (unused, held-out experiments), benchmark |
gt_video_meta.json |
8,160 | duration, fps and frame count of the original video, keyed <experiment>/<sample_id>, for every RL and val sample |
Videos.
| Folder | Count | Format | Role |
|---|---|---|---|
videos/ |
29,558 | 360×180, 10 fps, first 8 s, H.264 CRF 23 | Model input. Same pipeline as the benchmark: re-encoding benchmark samples gives byte-identical files. |
ref_videos/ |
8,160 | original resolution (e.g. 1440×720), 30 fps, about 12 s | The unmodified dataset videos of the RL and val samples. The GRPO reward and the diagnosis compare generated videos against these. |
Both folders are shipped as tar shards (videos.tar, ref_videos-00..07.tar).
Row format (ms-swift messages):
{"messages": [{"role": "system", "content": "..."},
{"role": "user", "content": "<video>\nWatch the video (scipy simulation) ..."},
{"role": "assistant", "content": "@@COT_BEGIN@@\n{\"simulation\": ..., \"physical_law\": {...}}\n@@COT_END@@\n\n@@CODE_BEGIN@@\n...\n@@CODE_END@@"}],
"videos": ["videos/scipy/<experiment>/<sample_id>.mp4"]}
Rows in rl_train.jsonl and rl_val.jsonl have no assistant turn. Instead they carry fields that are passed to the reward functions:
solution: the ground-truth code;gt_cot: the ground-truth CoT, as JSON;simulation;engine.
kubric: the full kubric dataset
kubric/ holds the complete kubric engine dataset as one tar per experiment, unpacked under dataset_kubric/:
- 75 experiments, 95,855 samples;
- video 30 fps, e.g. 480×480.
Each sample folder contains video.mp4, params.json, cot.json and simulation_code.py. kubric/manifest.csv lists every sample and flags the 1,125 samples used in the PhySimCode benchmark (in_benchmark=1). Exclude those samples if you train on kubric and evaluate on the benchmark. The data_v4 experiments do not use kubric.
Access
The dataset is gated. Request access on this page, then authenticate with hf auth login or export HF_TOKEN=....