Vero: An Open RL Recipe for General Visual Reasoning
What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) show such broad visual reasoning is within reach, but the recipe behind them remains unclear, locked behind proprietary reinforcement learning (RL) pipelines with non-public data. We introduce Vero, a family of fully open VLMs that matches or exceeds existing open-weight models across diverse visual reasoning tasks. We scale RL data and rewards across six broad task categories, constructing Vero-600K, a 600K-sample dataset from 59 datasets, and designing task-routed rewards that handle heterogeneous answer formats. Vero achieves state-of-the-art performance, improving over four base models by 3.7-5.5 points on average across VeroEval, our suite of 30 challenging benchmarks. Starting from Qwen3-VL-8B-Instruct, Vero outperforms Qwen3-VL-8B-Thinking on 23 of 30 benchmarks without additional proprietary thinking data. When trained from the same base model, Vero-600K exceeds existing RL datasets across task categories. Systematic ablations reveal that different task categories elicit qualitatively distinct reasoning patterns that transfer poorly in isolation, suggesting that broad data coverage is the primary driver of strong RL scaling. All data, code, and models are released.
69d46efc2c523d36431fb0cc
Gabriel Sarch
69d46efc2c523d36431fb0cd
Linrong Cai
69d46efc2c523d36431fb0ce
Qunzhong Wang
claimed_verified
2026-04-07T08:34:30.911Z
67a2ed64ed6024cc7b4f8acf
https://cdn-avatars.huggingface.co/v1/production/uploads/67a2ed64ed6024cc7b4f8acf/763S4FbWvnaYl2D8KyIsp.jpeg
Qunzhong WANG
qunwang13
user
qunwang13
69d46efc2c523d36431fb0cf
Haoyang Wu
69d46efc2c523d36431fb0d0
Danqi Chen
69d46efc2c523d36431fb0d1
Zhuang Liu
Vero: An Open RL Recipe for General Visual Reasoning
What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) show such broad visual reasoning is within reach, but the recipe behind them remains unclear, locked behind proprietary reinforcement learning (RL) pipelines with non-public data. We introduce Vero, a family of fully open VLMs that matches or exceeds existing open-weight models across diverse visual reasoning tasks. We scale RL data and rewards across six broad task categories, constructing Vero-600K, a 600K-sample dataset from 59 datasets, and designing task-routed rewards that handle heterogeneous answer formats. Vero achieves state-of-the-art performance, improving over four base models by 3.7-5.5 points on average across VeroEval, our suite of 30 challenging benchmarks. Starting from Qwen3-VL-8B-Instruct, Vero outperforms Qwen3-VL-8B-Thinking on 23 of 30 benchmarks without additional proprietary thinking data. When trained from the same base model, Vero-600K exceeds existing RL datasets across task categories. Systematic ablations reveal that different task categories elicit qualitatively distinct reasoning patterns that transfer poorly in isolation, suggesting that broad data coverage is the primary driver of strong RL scaling. All data, code, and models are released.
Vero is an open vision-language model family that achieves state-of-the-art visual reasoning performance through scaled reinforcement learning data across diverse tasks, demonstrating that broad data coverage drives strong RL scaling rather than isolated task-specific patterns.
vision-language models reinforcement learning visual reasoning Vero-600K task-routed rewards VeroEval Qwen3-VL-8B-Instruct Qwen3-VL-8B-Thinking