README.md

---
pretty_name: Training Data VID2
language:
  - zh
  - en
task_categories:
  - text-generation
tags:
  - sft
  - tool-use
  - video-evaluation
  - qwen3.5
---

# Training Data VID2

This dataset contains tool-using supervised fine-tuning (SFT) trajectories for
the video interval evaluation task in **ProRL-Agent-Server**. The trajectories
were primarily rolled out with `Qwen/Qwen3.5-122B-A10B-FP8` and are used to
train/evaluate the Qwen3.5-4B SFT experiments.

The source directory in the repository is:

```text
examples/video_eval_interval_multi_nodes/SFT_data_curation/sft_qwen35_122b/
```

Related code is located at:

```text
examples/video_eval_interval_multi_nodes/SFT_data_curation/
  step_1_convert_qwen35_rollouts_to_sft.py
  data_mixing_strategy_common.py
  data_mixing_strategy_band_wise_pareto.py
  data_mixing_strategy_max_n.py
  temp_running_script/build_reward_band_no_gt_datasets.py
  temp_running_script/build_reward_gte_0p3_no_gt_dataset.py

examples/video_eval_interval_multi_nodes/SFT_training/
  train_hf_sft.py
  run_hf_sft_with_periodic_eval.sh
  training_args.json
```

## Record format

Each `.jsonl` line is one complete multi-turn SFT trajectory. Important
top-level fields include:

- `messages`: system/user/assistant/tool turns. Assistant turns are the SFT
  targets; tool turns preserve the tool-grounded trajectory.
- `tools` and `tool_choice`: tool schemas and tool-selection configuration.
- `chat_template` and `chat_template_kwargs`: prompt formatting information.
- `model` and decoding fields such as `temperature`, `top_p`, `top_k`, and
  repetition penalties: rollout provenance, not necessarily training-time
  generation settings.
- `metadata`: source run, task/example/sample IDs, parsing status, reward,
  interval/tag metrics, reference label, parsed prediction, and token counts.

Some messages contain embedded visual payloads, so individual records and
files are unusually large. Use streaming line-by-line reads instead of loading
an entire JSONL file into memory.

```python
import json

with open(
    "original/0.7-1.0/"
    "qwen35_122b_sft_train_full_n16_tool_sft_high_quality.jsonl"
) as f:
    for line in f:
        row = json.loads(line)
        messages = row["messages"]
        reward = row["metadata"]["reward"]
        # train on / inspect one trajectory at a time
```

## Files included in this Hugging Face dataset

Only the following paths are uploaded:

```text
README.md
original/
  0.3-0.5/
  0.5-0.7/
  0.7-1.0/
```

The derived JSONL files stored at the local directory root are intentionally
not uploaded. This keeps the remote dataset limited to reusable source-level
converted trajectories; merged, sampled, deduplicated, and Pareto variants can
be regenerated in the ProRL-Agent-Server repository.

## `original/` reward bands

The reward intervals use an **inclusive lower bound**:

- `original/0.3-0.5/`: `[0.3, 0.5)`
- `original/0.5-0.7/`: `[0.5, 0.7)`
- `original/0.7-1.0/`: `[0.7, 1.0]`

Each band contains the same source categories when qualifying trajectories are
available:

| Filename pattern | Meaning |
|---|---|
| `m40_qwen35_122b_sft_train_full_n16_m40_4gpu_max65_*` | General training rollouts produced on the m40 environment, with the run-specific `n16`, four-GPU, and max-65 settings. |
| `qwen35_122b_sft_train_full_n16_*` | General training rollouts with up to 16 sampled trajectories per case. |
| `m40_qwen35_122b_sft_difficult_lv2_n32_*` | Difficult-level-2 training rollouts with up to 32 samples per case. |
| `qwen35_122b_sft_difficult_lv1_n16_*` | Difficult-level-1 training rollouts with up to 16 samples per case. |
| `qwen35_sft_val_n64_2x4_after_random_*` | Validation trajectories generated by the `n64_2x4_after_random` rollout run. |
| `qwen35_sft_gt_guided_difficult_lv1_val_n20_*` | Difficult-level-1 validation trajectories rolled out with GT as privileged context. |
| `qwen35_sft_gt_guided_difficult_lv3_n8_*` | Difficult-level-3 trajectories rolled out with GT as privileged context. |
| `*.lengths.json` | Precomputed Qwen3.5-4B token lengths for the adjacent JSONL file, used for length grouping/bucketing. |

All JSONL filenames end in `tool_sft_high_quality`, meaning that they contain
converted tool-trajectory SFT records that passed the applicable conversion
and quality checks. The `m40` component records the rollout environment; `n8`,
`n16`, `n20`, and `n32` record the run's sampling count.

Two paths are intentionally present but empty because that reward band had no
matching GT-guided rows:

```text
original/0.5-0.7/qwen35_sft_gt_guided_difficult_lv1_val_n20_tool_sft_high_quality.jsonl
original/0.5-0.7/qwen35_sft_gt_guided_difficult_lv3_n8_tool_sft_high_quality.jsonl
```

Do not interpret these zero-byte placeholders as corrupt uploads.

## Regenerating the derived experiment files

The repository's data-curation scripts merge/filter these original sources to
produce experiment-ready no-GT-guided files. For example, the local
`[0.5, 0.7)` experiment uses the generated files:

```text
TRAIN_FILE=qwen35_122b_sft_reward_0p5_to_0p7_no_gt_guided_train.jsonl
VALIDATION_FILE=qwen35_122b_sft_reward_0p5_to_0p7_no_gt_guided_val.jsonl
LENGTHS_FILE=qwen35_122b_sft_reward_0p5_to_0p7_no_gt_guided_train.lengths.json
```

The repository launchers resolve these paths relative to
`examples/video_eval_interval_multi_nodes/SFT_data_curation/sft_qwen35_122b/`.
These generated root-level files are **not part of this Hugging Face upload**.

## Caveats

- The dataset contains large embedded media/tool contexts and totals hundreds
  of GB; clone/download only the files needed for a specific experiment.
- Absolute local paths inside `metadata` are provenance from the source machine
  and are not expected to resolve elsewhere.
- Reward-band datasets are trajectory-level datasets. Multiple rows can share
  the same underlying example/case.
- GT-guided trajectories can leak privileged information into assistant-side
  reasoning even after the explicit GT prompt block is removed. Regenerate and
  use the local `no_gt_guided` derivatives for controlled no-privilege
  experiments.
- Verify data rights, privacy requirements, and downstream usage constraints
  before redistributing or using this dataset outside the research project.