Vero 2.5M unfiltered [!Note] This repository contains the full unfiltered dataset used to construct Vero 600k and Vero 1.6M, before question and answer filtering. Note that task categories are not balanced in this dataset. Vero is a fully open reinforcement learning (RL) recipe for training and evaluating multi task visual reasoning with vision language models. This repository contains the Vero 2.5M unfiltered dataset, a curation of 2.5M reinforcement learning samples from 59 datasets across 6 diverse visual reasoning categories. Highlights Scale : 2.5M RL samples from 59 datasets. Diversity : Covers 6 broad categories: STEM Reasoning, Chart & OCR, Spatial & Action, Knowledge & Recognition, Grounding & Counting, and Instruction Following. Task Routed Rewards : Designed to handle heterogeneous answer formats across diverse tasks. Open Recipe : Fully open release of models, training code, evaluation suite, and dataset. Dataset Structure The dataset is organized into six broad task categories: 1. STEM reasoning 2. Chart and OCR 3. Spatial reasoning and action 4. Knowledge and recognition 5. Grounding, counting, and visual search 6. Captioning and instruction following For detailed dat…
Runs entirely in your browser via DuckDB-Wasm — this dataset's real data file is loaded once, then queried locally. Nothing is sent to a server.
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