ProxyThinker: Test-Time Guidance through Small Visual Reasoners

Recent advancements in reinforcement learning with verifiable rewards have pushed the boundaries of the visual reasoning capabilities in large vision-language models (L VLMs). However, training L VLMs with reinforcement fine-tuning (RFT) is computationally expensive, posing a significant challenge to scaling model size. Recent advances in large language models have led to the development of systems capable of extended reasoning and deliberation, often referred to as "slow-thinking" models, such as OpenAI-o1 (Jaech et al., 2024), DeepSeek-R1 (Guo et al., 2025), and QwQ (Team, 2025). Unlike "fast-thinking" models such as GPT -4o (Hurst et al., 2024), "slow-thinking" models usually engage in multi-step self-reflection to produce an answer that resembles the thorough thinking process that humans make before producing a final answer for non-trivial problems. These models have achieved remarkable success in complex problem-solving benchmarks, particularly in mathematical and scientific reasoning domains (Shao et al., 2024b; Zeng et al., 2025; Y u et al., 2025). Recent research has also extended such reflective reasoning to multimodal tasks (Huang et al., 2025; Deng et al., 2025; Y ang et al., 2025; Zhou et al., 2025; Wang et al., 2025a), pushing large vision-language models (L VLMs) toward greater performance in scenarios that require structured and contextual understanding across modalities. Many of the most effective "slow-thinking" models rely on reinforcement learning with verifiable rewards (RL VR) (Face, 2025; Su et al., 2025; Wei et al., 2025a), a reinforcement fine-tuning (RFT) framework that encourages the model to generate intermediate reasoning steps that lead to a correct answer for automatically verifiable tasks.