基於非對稱相互變分學習的多模態連續推理

摘要

多模态大语言模型（MLLMs）常受限于语言空间瓶颈，迫使复杂的视觉推理被压缩为离散词元，从而可能丢失感知细节。一种有前景的替代方案是连续潜在推理，其目标在于发现连接多模态查询与最终答案的隐式推理路径。然而，这引入了严重的训练-推理不匹配：训练阶段依赖于真实答案的后验，能够利用答案相关的捷径。标准变分训练迫使推理阶段的先验去模仿一个在测试时无法获取信息的后验，从而导致性能不佳。为解决这一问题，我们提出非对称互变分学习（AMVL），该框架通过双向校准目标消除这种不匹配。正向KL散度训练与目标无关的先验去匹配后验，而新颖的反向KL散度则同时正则化后验，防止其坍缩至推理不兼容的区域，并缓解这种“答案泄漏”。我们提供了理论分析，将这种泄漏形式化为先验污染，并证明我们的双KL目标能减轻该问题。我们将AMVL实例化于一个潜在集成的MLLM中，实验表明它持续优于强大的离散推理和潜在推理基线，在复杂的BLINK基准测试上平均得分提升+10.83，并在个别推理任务上取得高达+32.00的提升，分析也证实了潜在空间稳定性的改善。

English

Multimodal Large Language Models (MLLMs) are often constrained by a language-space bottleneck, forcing complex visual reasoning into discrete tokens which can lose perceptual nuance. A promising alternative is continuous latent reasoning, where the goal is to discover implicit reasoning pathways that bridge the multimodal query and the final answer. However, this introduces a severe train-inference mismatch: a training-time posterior, conditioned on the ground-truth answer, can exploit answer-dependent shortcuts. Standard variational training then forces the inference-time prior to mimic a posterior that has access to information unavailable at test time, leading to poor performance. To address this, we propose Asymmetric Mutual Variational Learning (AMVL), a framework that resolves this mismatch via a bidirectional calibration objective. A forward KL divergence trains the target-agnostic prior to match the posterior, while a novel reverse KL divergence simultaneously regularizes the posterior, preventing it from collapsing into inference-incompatible regions and mitigating this ``answer leakage''. We provide theoretical analysis formalizing this leakage as prior contamination and prove that our dual-KL objective reduces it. We instantiate AMVL in a latent-integrated MLLM and show that it consistently outperforms strong discrete and latent-reasoning baselines, improving the average score on the complex BLINK benchmark by +10.83 and achieving gains of up to +32.00 on individual reasoning tasks, with analyses confirming improved latent-space stability.