---
license: apache-2.0
task_categories:
  - video-text-to-text
  - question-answering
language:
  - en
tags:
  - video
  - multi-hop-reasoning
  - rlvr
  - verifiable-rewards
  - reinforcement-learning
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: val
        path: data/val.parquet
---

# HopChain v4.0 + Vript — Harder Multi-Hop Video Reasoning with Verifiable Answers

**28,865 multi-hop video questions** over **7,396 videos**, each with an **integer ground-truth answer**
checkable by rule (no LLM judge at train time). Built for **RLVR** training.

This dataset combines two sources:
- **HopChain v4.0** (6,692 QAs / 1,748 videos): 1080p caption-grounded, conditional-predicate multi-hop questions over diverse YouTube videos
- **Vript** (22,173 QAs / 5,648 videos): dense scene-annotated videos (>3min), re-captioned with 397B and processed through the same HopChain pipeline

Both sources use the same question format, system prompt, and reward contract.

## Splits

| Split | Rows | HopChain | Vript | Videos | Hops (3/4/5/6/7) | Solve Rate |
|-------|------|----------|-------|--------|-------------------|------------|
| Train | 25,975 | 6,004 | 19,971 | 6,657 | 523 / 4,957 / 9,438 / 8,013 / 3,044 | 0.0081 |
| Val   | 2,890 | 688 | 2,202 | 739 | 46 / 557 / 1,071 / 879 / 337 | 0.0088 |
| **Total** | **28,865** | **6,692** | **22,173** | **7,396** | **569 / 5,514 / 10,509 / 8,892 / 3,381** | **0.0082** |

Video-disjoint split (seed=42): no video appears in both train and val.

## What is HopChain v4.0

HopChain v4.0 is the difficulty successor to v3.4. Each question chains **3–6 conditional-predicate hops** (mode 4–5). A hop observes one robust fact about a scene and a binary rule turns it into a number — `if <fact> then A else B` — aggregated with `+ *` into a single verifiable integer.

### 11 reasoning families
- **Original (6):** `existence`, `color`, `action`, `cooccur`, `setting`, `arithmetic`
- **New cross-scene (5):** `order`, `first_last`, `attr_match`, `recurrence`, `state_change`

### Difficulty gate
Too-easy probe = training model (`Qwen3-VL-8B-Instruct`, 140 frames), dropped if solve_rate ≥ 6/8.

## What is Vript

[Vript](https://huggingface.co/datasets/Mutonix/Vript) provides ~11K high-quality YouTube videos with dense scene-level annotations (shot type, camera movement, content, voiceover). We use **8,046 videos >3 minutes** (median 7m12s, mean 9m), re-captioned with 397B and processed through the same HopChain pipeline.

Vript's longer videos (3–7 min vs 20–60s for HopChain) provide richer temporal reasoning opportunities.

## Schema

Each row is a complete verl-compatible prompt:

```json
{
  "prompt": [
    {"role": "system", "content": "You are a careful reasoning assistant..."},
    {"role": "user", "content": "<video>\n<question>"}
  ],
  "images": [],
  "videos": [{"type": "video", "video": "file:///path/to/video.mp4", "fps": 1,
              "max_frames": 140, "min_pixels": 3136, "max_pixels": 50176}],
  "data_source": "video_multihop_prototype",
  "ability": "video_multihop_reasoning",
  "reward_model": {"ground_truth": "42", "style": "rule"},
  "extra_info": {
    "reward_type": "numeric",
    "answer": "42",
    "answer_type": "numeric",
    "num_hops": 5,
    "hop_types": ["order", "cooccur", "action", "color", "arithmetic"],
    "question_id": "VIDEOID_hq000",
    "video_id": "VIDEOID",
    "solve_rate": 0.0,
    "difficulty_band": "hard"
  }
}
```

### Key fields

- **`reward_model.ground_truth`**: integer answer (exact-match reward)
- **`extra_info.num_hops`**: number of conditional-predicate observation hops
- **`extra_info.hop_types`**: list of reasoning types per hop
- **`extra_info.solve_rate`**: fraction of 8 runs where training model (Qwen3-VL-8B-Instruct, 140f) solved it — **use for curriculum sampling**
- **`extra_info.difficulty_band`**: `"hard"` (99.5%), `"medium"`, `"easy"`

### Video paths

- **HopChain videos:** `file:///scratch/users/astar/ares/suns1/trung/videoreason/proxy_140f50k/{VIDEOID}.mp4` (140-frame proxies, 50176 max pixels)
- **Vript videos:** `file:///scratch/users/astar/ares/suns1/tmp/vript/videos/{VIDEOID}.mp4` (original resolution)

Both use `fps=1, max_frames=140, min_pixels=3136, max_pixels=50176`.

## Funnel

### HopChain v4.0
| Stage | Count |
|-------|-------|
| Input videos | 1,818 |
| Videos processed | 1,787 (98.3%) |
| Total candidates | 14,021 |
| Judge survivors | 7,539 (53.8%) |
| Final keepers | 6,692 (3.74/vid) |

### Vript
| Stage | Count |
|-------|-------|
| Input videos | 8,046 |
| Videos processed | ~5,800 |
| Final keepers | 22,173 (3.82/vid) |

## Pipeline

1. **Caption:** 397B-FP8 generates dense captions from video frames (HopChain: 1080p oracle; Vript: re-captioned from scene annotations)
2. **Generate:** 397B-FP8 produces 5 candidate questions per video
3. **Judge:** 27B scores candidates, selects best
4. **Too-easy filter:** 8B-VL probes each question (8 runs), drops if solve_rate ≥ 6/8
5. **Blind-solver filter:** removes language-only shortcuts (rate 0.071)

## Curriculum sampling

The `solve_rate` field enables curriculum training:
- **Warm-up:** rows where solve_rate > 0 (easier, 3–4 hop, simpler families)
- **Anneal:** into the 99.5% hard tail (solve_rate = 0, 5–7 hop)

## Notes

1. **Mixed caption oracle:** HopChain videos: 144/1,818 captioned by 122B, rest by 397B-FP8. Vript: all re-captioned by 397B.
2. **Generation never sees a frame:** caption is the only ground truth. Blind-solver rate (0.071) = shortcut ceiling.
3. **Video-disjoint split:** ensures no video leakage between train and val.

## Reproduction

Built from the [VideoReasoningBench](https://github.com/ngquangtrung57/VideoReasoningBench) HopChain v4.0 pipeline.

## Citation

```bibtex
@misc{hopchain2026,
  title={HopChain: Harder Multi-Hop Video Reasoning with Verifiable Answers},
  author={Trung, Ng Quang},
  year={2026},
  howpublished={\\url{https://huggingface.co/datasets/ngqtrung/hopchain-v40-1080p}}
}
```