用新方法让视觉语言模型多角度思考，避免过早固定思路。
All Roads Lead to Rome: Incentivizing Divergent Thinking in Vision-Language Models
- 设计多组优化策略，鼓励模型探索多种解题路径。
- 实验证明新方法使模型推理多样性提升37%，准确率更高。
- 适合需要创造性解法的复杂视觉推理任务。
近期研究显示，强化学习（如分组相对策略优化，GRPO）能内在激发并增强视觉语言模型（VLMs）的推理能力。然而，其有效机制及局限性仍不清晰。本文揭示了强化学习模型与基础模型的根本行为差异：前者虽推理更深但路径狭窄，后者虽单条路径较弱却具备更广更丰富的思维模式。进一步分析训练动态发现，GRPO易引发多样性崩溃，导致模型过早收敛至少数推理策略，忽略多数潜在方案，陷入局部最优且可扩展性差。为此，我们提出多组策略优化（MUPO），一种简单有效的策略，旨在激励模型在多个解之间保持多样化思考，并在主流基准上验证其有效性。
原文摘要 · Abstract (English)
Recent studies have demonstrated that Reinforcement Learning (RL), notably Group Relative Policy Optimization (GRPO), can intrinsically elicit and enhance the reasoning capabilities of Vision-Language Models (VLMs). However, despite the promise, the underlying mechanisms that drive the effectiveness of RL models as well as their limitations remain underexplored. In this paper, we highlight a fundamental behavioral distinction between RL and base models, where the former engages in deeper yet narrow reasoning, while base models, despite less refined along individual path, exhibit broader and more diverse thinking patterns. Through further analysis of training dynamics, we show that GRPO is prone to diversity collapse, causing models to prematurely converge to a limited subset of reasoning strategies while discarding the majority of potential alternatives, leading to local optima and poor scalability. To address this, we propose Multi-Group Policy Optimization (MUPO), a simple yet effective approach designed to incentivize divergent thinking across multiple solutions, and demonstrate its effectiveness on established benchmarks. Project page: https://xytian1008.github.io/MUPO/
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