The datasets behind our Qwen3-VL-8B video RLVR runs: the base 24f/100k mixture and the HopChain v5 multi-hop corpora.
Nguyen Quang Trung
ngqtrung
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LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal
Training updated a collection 5 days ago
Video RLVR — final training data updated a collection 5 days ago
Video RLVR — final training dataOrganizations
Qwen3-VL-8B GRPO RLVR checkpoints from a token-dropout exploration study. OMR ppexplore=winner (0.714); video ~0.485 dead-heat.
Raw multi-style distilled traces, the pre-distillation template seed, and the never-trained real-audio eval.
Every published checkpoint of the 8B video-QA GRPO campaign, ranked by core-3 mean_accuracy (5,645 rows). Untrained base = 0.4426.
Curated SFT + GRPO RL datasets (video MC-QA, OMR math-image, OpenMMReasoner-RL, Vero) for Qwen3-VL-8B post-training.
Train-ready VMAR datasets: teacher-distilled SFT corpus, RL prompt set, and the curated in-loop eval benchmark.
The datasets behind our Qwen3-VL-8B video RLVR runs: the base 24f/100k mixture and the HopChain v5 multi-hop corpora.
Every published checkpoint of the 8B video-QA GRPO campaign, ranked by core-3 mean_accuracy (5,645 rows). Untrained base = 0.4426.
Qwen3-VL-8B GRPO RLVR checkpoints from a token-dropout exploration study. OMR ppexplore=winner (0.714); video ~0.485 dead-heat.
Curated SFT + GRPO RL datasets (video MC-QA, OMR math-image, OpenMMReasoner-RL, Vero) for Qwen3-VL-8B post-training.
Raw multi-style distilled traces, the pre-distillation template seed, and the never-trained real-audio eval.
Train-ready VMAR datasets: teacher-distilled SFT corpus, RL prompt set, and the curated in-loop eval benchmark.