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Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning
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While state-of-the-art vision-language models (VLMs) have demonstrated remarkable capabilities in complex visual-text tasks, their success heavily relies on massive model scaling, limiting their practical deployment. Small-scale VLMs offer a more practical alternative but face significant challenges when trained with traditional supervised fine-tuning (SFT), particularly in two aspects: out-of-domain (OOD) generalization and reasoning abilities, which significantly lags behind the contemporary Large language models (LLMs). To address these challenges, we propose Curriculum Reinforcement Finetuning (Curr-ReFT), a novel post-training paradigm specifically designed for small-scale VLMs. Inspired by the success of reinforcement learning in LLMs, Curr-ReFT comprises two sequential stages: (1) Curriculum Reinforcement Learning, which ensures steady progression of model capabilities through difficulty-aware reward design, transitioning from basic visual perception to complex reasoning tasks; and (2) Rejected Sampling-based Self-improvement, which maintains the fundamental capabilities of VLMs through selective learning from high-quality multimodal and language examples. Extensive experiments demonstrate that models trained with Curr-ReFT paradigm achieve state-of-the-art performance across various visual tasks in both in-domain and out-of-domain settings. Moreover, our Curr-ReFT enhanced 3B model matches the performance of 32B-parameter models, demonstrating that efficient training paradigms can effectively bridge the gap between small and large models.
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Cited by 13 Pith papers
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S-GRPO: Unified Post-Training for Large Vision-Language Models
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SyncLoop jointly evolves multimodal training data and model capability through alternating SFT and RL, selecting error-prone samples to improve geometry reasoning.
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MM-R5, a 7B multimodal re-ranker trained with SFT and GRPO, achieves state-of-the-art page-level recall on MMDocIR by generating per-page reasoning chains.
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ZeroGUI uses VLM-generated tasks and VLM-estimated rewards with two-stage GRPO to improve GUI agent success rates on OSWorld and AndroidLab without human annotations.
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A curriculum reinforcement-learning framework with in-context refocus examples improves camouflaged object classification and detection for a vision-language model, and the authors report surpassing human performance ...
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A self-paced GRPO variant that adaptively reweights visual, temporal, and text-alignment reward components as the generator improves reports small VBench gains over static-reward baselines.
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ChartReasoner converts charts into executable ECharts code, distills long-chain reasoning traces from that code, and trains a 7B multimodal model with SFT and GRPO to improve chart question answering.
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WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement Learning
WeThink, a 120K-image QA dataset with AI-generated reasoning paths, combined with hybrid-reward reinforcement learning, improves a 7B vision-language model across 14 benchmarks.
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UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning
UniVG-R1 uses CoT supervised fine-tuning plus GRPO with difficulty-aware reweighting to make Qwen2-VL substantially better at multi-image, reasoning-based visual grounding.
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A survey paper maps how external tools are used to augment multimodal large language models across data, tasks, evaluation, and future directions.
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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models
A survey-style position paper claims that reinforcement fine-tuning powers reasoning in multimodal LLMs, summarizing over a hundred recent works and proposing five future research directions.
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A survey categorizing deep reinforcement learning and direct preference optimization methods for aligning large vision-language models, with tables of studies and datasets and no new experimental result.
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