--- license: other task_categories: - visual-question-answering language: - en tags: - multimodal - region-of-interest - reinforcement-learning size_categories: - 100K. Images: [`YuhengSSS/RoITraining`](https://huggingface.co/datasets/YuhengSSS/RoITraining). ## Contents ``` sdrpn_corpora/ stage 1 — self-generated response corpora qwen3_5_4b_response_corpus.jsonl 49,498 rows qwen3_5_9b_response_corpus.jsonl 49,999 rows gemma4_12b_response_corpus.jsonl 49,498 rows (tier 560) rl_pools/ stage 2 — RL pools (7,000 rows each) candidates_visualcot_50k.jsonl 50,000 candidate QAs the pools are drawn from rl_pool_qwen3_5_4b.jsonl rl_pool_qwen3_5_9b.jsonl rl_pool_qwen2_5_vl_7b.jsonl rl_pool_gemma4_12b.jsonl ev_maps/ cached response→image evidence maps (7,000 .pt each) ev_maps_qwen3_5_4b.tar ev_maps_qwen3_5_9b.tar ev_maps_qwen2_5_vl_7b.tar ev_maps_gemma4_12b.tar ``` ## Download and layout ```bash hf download YuhengSSS/VisionRL2-data --repo-type dataset --local-dir data/VisionRL2-data mkdir -p data/ev_maps for t in data/VisionRL2-data/ev_maps/*.tar; do tar -xf "$t" -C data/ev_maps; done ``` giving `data/ev_maps/ev_maps_qwen3_5_4b/00000012.pt`, and so on. Images come from [`YuhengSSS/RoITraining`](https://huggingface.co/datasets/YuhengSSS/RoITraining) (tars: `gqa`, `textvqa`, `spdocvqa` → `DocVQA`, `infographicsvqa`). Extract them so that `DATASET_ROOT` looks like: ``` DATASET_ROOT/ gqa/images/ textvqa/train_images/ DocVQA/ infographicsvqa/infographicsvqa_images/ ``` `DATASET_ROOT` defaults to `datasets`; individual roots can be overridden with `DS_IMAGE_ROOTS="gqa=/abs/path,docvqa=/abs/path,..."`. ## Regenerating these files Every file here can be rebuilt from the candidates plus your own checkpoints, using the release repo: | target | command | |---|---| | Qwen3.5 corpora | `data_prep/build_corpus_qwen3_5.sh` (split → generate v1/v2 → merge) | | Gemma-4 corpus | `scripts/train_sdrpn_gemma4.sh` stages `gen` + `merge` | | Qwen3.5 pools | `data_prep/build_pool_qwen3_5.sh` (filter → compose → evidence → maps) | | Qwen2.5-VL pool | `data_prep/build_pool_qwen2_5_vl.sh` | | Gemma-4 pool | `data_prep/build_pool_gemma4.sh` | ## Row schemas ### SD-RPN corpus row (`sdrpn_corpora/`) One row per QA; the response is generated by the **frozen backbone itself**, and the pseudo-label is the response-to-image attention of that response. | field | meaning | |---|---| | `dataset` | source tag: `gqa` / `textvqa` / `docvqa` / `infographicsvqa` — selects the image root | | `image` | image path **relative to that dataset's image root** | | `question` | raw question | | `prompted_question` | the exact prompt that was fed to the backbone (prompt style baked in) | | `answer` | gold short answer | | `response` | the backbone's own response (the pseudo-label is read off its attention) | The `prompted_question` of a row fixes its prompt style: `gqa` rows carry the bounding-box task suffix and `textvqa` rows the "single word or phrase" suffix (style v1, square-padded images, up to 1,024 visual tokens), while `docvqa` and `infographicsvqa` rows carry the `[Visual Evidence] … [Answer]` prompt (style v2, no square padding, up to 576 visual tokens). Both passes decode greedily with 512 new tokens; rows whose response came back empty were dropped. The Qwen corpora carry the extra VisualCoT provenance fields (`width`, `height`, `bboxs`, `split`, `full_answer`, `reasoning`, `thought`, `possible_answers`); the trainer ignores them. The pseudo-label *style* per row (`v1` = mean-over-response-tokens map, `v2` = single-region peak-ratio union) is derived from the `dataset` tag via `DS_TO_LABEL_VERSION` in `qwen-vl-finetune/qwenvl/data/__init__.py` (`gqa`/`textvqa` → v1, `docvqa`/`infographicsvqa` → v2). The **Gemma** corpus instead carries the style as an explicit per-row field and uses full dataset-root-relative image paths: | field | meaning | |---|---| | `dataset`, `question`, `answer`, `response` | as above | | `image` | path relative to `DATASET_ROOT` (e.g. `gqa/images/2337160.jpg`) | | `question_id`, `src_row` | provenance into `candidates_visualcot_50k.jsonl` | | `version` | `v1` (gqa + textvqa) or `v2` (docvqa + infographicsvqa) — consumed by `qwen_src/gemma4_unified/data_gemma_stage1.py` | ### RL pool row (`rl_pools/rl_pool_*.jsonl`) | field | meaning | |---|---| | `sample_id` | index into `candidates_visualcot_50k.jsonl` | | `dataset` | source tag (image root selector) | | `image` | image path relative to that dataset's image root | | `question` | question (the eval answer suffix is appended by the loader) | | `gold_answer` | gold answer scored by the frozen reader | | `feat_hw` | `[H, W]` of the SD-RPN heatmap grid at pool-build resolution | | `branch` | `K==1` / `K>=2` — number of connected components in the stage-1 proposal | | `K`, `K_topR` | components found / components kept as actions | | `n_actions` | size of the enumerated action set | | `reward_mean`, `reward_std` | stage-1 reward statistics over that action set (the ranking signal) | | `ev_maps_path` | `ev_maps_/.pt` — the cached evidence maps for this row | `candidates_visualcot_50k.jsonl` is the raw 50k VisualCoT candidate set the pools are drawn from (`gqa` 20k, `textvqa` 10k, `docvqa` 10k, `infographicsvqa` 10k). ### `EV_MAPS_ROOT` convention `ev_maps_path` is **relative**. The loader (`qwen-vl-finetune/qwenvl/train/region_level_grpo/dataset.py`) resolves it in order: as given, then `$EV_MAPS_ROOT/`, then `$EV_MAPS_ROOT/`, then the pool jsonl's own directory. With the layout above, set: ```bash export EV_MAPS_ROOT=data/ev_maps ``` Each `.pt` holds `{"maps": uint8 tensor (n_layers, Hg, Wg)}` — the frozen backbone's response-to-image attention at the probed layers, binarised per layer. They feed the **additive (source-map) group** of the RL objective, which recovers evidence the policy never proposed. A row that declares an `ev_maps_path` whose file cannot be found is a hard error (a silent miss would turn that group off). ## How the 7k pools were selected The pool is built with the backbone's **own** SD-RPN (stage-1) checkpoint: 1. Run the backbone's SD-RPN over the 50k candidates; drop `gqa` and `chartqa`, and drop rows whose gold region covers more than 10% of the image. 2. Enumerate the action set (top `R = 6` components: the empty action plus singleton drops), score every action with the frozen reader, and keep the top 20% of rows by **per-sample reward standard deviation** — rows where the choice of region actually moves the reward. 3. Compose the final pool: **5,000 InfographicVQA + 1,000 TextVQA + 1,000 DocVQA** = 7,000. 4. Cache the response-to-image evidence maps for every kept row (`ev_maps/`), from the backbone's own evidence responses. The heatmap gate at step 1 differs per family (Qwen3.5: fixed threshold 0.02; Qwen2.5-VL-7B and Gemma-4: peak-ratio). **Provenance note.** `rl_pool_qwen3_5_4b`, `rl_pool_qwen3_5_9b` and `rl_pool_gemma4_12b` were each selected by their own SD-RPN and are therefore different row sets (the 4B and 9B pools share 2,780 of 7,000 rows). `rl_pool_qwen2_5_vl_7b` is the exception: it reuses the Qwen3.5-4B **row selection** verbatim (identical 7,000 `sample_id`s and identical step-1/2 statistics, reordered) and only steps 3–4 are its own — the evidence responses and cached attention maps in `ev_maps_qwen2_5_vl_7b.tar` come from Qwen2.5-VL-7B itself. That is what the paper's 7B run trained on. The release repo ships the full pipeline (`data_prep/build_pool_qwen3_5.sh`, `build_pool_qwen2_5_vl.sh`, `build_pool_gemma4.sh`) so the pools can be regenerated on your own checkpoint. ## License and provenance The QAs and images are derived from [Visual-CoT](https://github.com/deepcs233/Visual-CoT) and, through it, from **GQA**, **TextVQA**, **DocVQA (SP-DocVQA)** and **InfographicVQA**. All original licenses and terms of those datasets apply; this release adds only model-generated responses, region statistics and cached attention maps, and is intended for **research use**. No images are redistributed here — only jsonl rows and `.pt` caches that reference them by relative path. ## Citation ```bibtex @article{shi2026visionrl2, title = {Region-Level Policy Optimization for Fine-grained MLLM Perception}, author = {Shi, Yuheng and Pei, Xiaohuan and Dong, Minjing and Xu, Chang}, journal = {arXiv preprint arXiv:2609.19745}, year = {2026} } @inproceedings{shi2026sdrpn, title = {Catching the Details: Self-Distilled RoI Predictors for Fine-Grained MLLM Perception}, author = {Shi, Yuheng and Pei, Xiaohuan and Dong, Minjing and Xu, Chang}, booktitle = {ICLR}, year = {2026} } ```