Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual
  Search
Recent advances in large multimodal models have leveraged image-based tools
with reinforcement learning to tackle visual problems. However, existing
open-source approaches often exhibit monotonous reasoning patterns and allow
only a limited number of interaction turns, making them inadequate for
difficult tasks that require trial-and-error exploration. In this work, we
address this limitation by scaling up tool-based interactions and introduce
Mini-o3, a system that executes deep, multi-turn reasoning -- spanning tens of
steps -- and achieves state-of-the-art performance on challenging visual search
tasks. Our recipe for reproducing OpenAI o3-style behaviors comprises three key
components. First, we construct the Visual Probe Dataset, a collection of
thousands of challenging visual search problems designed for exploratory
reasoning. Second, we develop an iterative data collection pipeline to obtain
cold-start trajectories that exhibit diverse reasoning patterns, including
depth-first search, trial-and-error, and goal maintenance. Third, we propose an
over-turn masking strategy that prevents penalization of over-turn responses
(those that hit the maximum number of turns) during reinforcement learning,
thereby balancing training-time efficiency with test-time scalability. Despite
training with an upper bound of only six interaction turns, our model generates
trajectories that naturally scale to tens of turns at inference time, with
accuracy improving as the number of turns increases. Extensive experiments
demonstrate that Mini-o3 produces rich reasoning patterns and deep thinking
paths, effectively solving challenging visual search problems.
68c0d77d3912ed54cf543201
Xin Lai
68c0d77d3912ed54cf543202
Junyi Li
claimed_verified
2025-09-12T16:18:39.485Z
63aaf2a2a4bdd629b7eb2b5b
https://cdn-avatars.huggingface.co/v1/production/uploads/63aaf2a2a4bdd629b7eb2b5b/WOa3nAUNy5D3MsFUV9B8Z.jpeg
Junyi Li
user
ProvenceStar
68c0d77d3912ed54cf543203
Wei Li
68c0d77d3912ed54cf543204
Tao Liu
68c0d77d3912ed54cf543205
Tianjian Li
68c0d77d3912ed54cf543206
Hengshuang Zhao
Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual
  Search
Recent advances in large multimodal models have leveraged image-based tools
with reinforcement learning to tackle visual problems. However, existing
open-source approaches often exhibit monotonous reasoning patterns and allow
only a limited number of interaction turns, making them inadequate for
difficult tasks that require trial-and-error exploration. In this work, we
address this limitation by scaling up tool-based interactions and introduce
Mini-o3, a system that executes deep, multi-turn reasoning -- spanning tens of
steps -- and achieves state-of-the-art performance on challenging visual search
tasks. Our recipe for reproducing OpenAI o3-style behaviors comprises three key
components. First, we construct the Visual Probe Dataset, a collection of
thousands of challenging visual search problems designed for exploratory
reasoning. Second, we develop an iterative data collection pipeline to obtain
cold-start trajectories that exhibit diverse reasoning patterns, including
depth-first search, trial-and-error, and goal maintenance. Third, we propose an
over-turn masking strategy that prevents penalization of over-turn responses
(those that hit the maximum number of turns) during reinforcement learning,
thereby balancing training-time efficiency with test-time scalability. Despite
training with an upper bound of only six interaction turns, our model generates
trajectories that naturally scale to tens of turns at inference time, with
accuracy improving as the number of turns increases. Extensive experiments
demonstrate that Mini-o3 produces rich reasoning patterns and deep thinking
paths, effectively solving challenging visual search problems.
Mini-o3, a system for deep, multi-turn reasoning in visual search tasks, uses an iterative data collection pipeline and over-turn masking strategy to achieve state-of-the-art performance with rich reasoning patterns.
reinforcement learning Visual Probe Dataset iterative data collection pipeline depth-first search trial-and-error goal maintenance over-turn masking strategy