While current Multimodal Large Language Models (MLLMs) have demonstrated
proficiency in reasoning tasks such as mathematics and logic, their capacity
for long-chain reflective reasoning, a prerequisite for solving complex
real-world problems, remains largely underexplored. In this work, we first
conduct an extensive empirical investigation to evaluate this capability.
Leveraging a carefully designed data synthesis engine, we construct MM-HELIX, a
multimodal benchmark consisting 1,260 samples of 42 challenging synthetic tasks
that require iterative thinking and backtracking. Empirical results on this
benchmark reveal that existing MLLMs exhibit significant performance deficits
in long-chain reflective reasoning. To address this limitation, we generate
post-training data and further explore learning paradigms for exploiting such
data. We first develop the Step-Elicited Response Generation pipeline to create
MM-HELIX-100K, a large-scale dataset of 100k high-quality, reflective reasoning
traces for instruction-tuning stage. Given that standard Reinforcement Learning
fails on complex tasks due to sparse reward signals and catastrophic forgetting
after Supervised Fine-Tuning, we propose Adaptive Hybrid Policy Optimization
(AHPO), a novel training strategy that dynamically unifies offline supervision
and online optimization into a single stage. This strategy enables the model to
learn from expert data when rewards are sparse and conduct independent
exploration once proficient. When applied to the Qwen2.5-VL-7B baseline, our
method achieves a +18.6\% accuracy improvement on MM-HELIX benchmark and
demonstrates strong generalization with a +5.7\% average performance gain on
general mathematic and logic tasks. Our work demonstrate that reflective
reasoning in MLLMs can be effectively learned and generalized, paving the way
for developing more capable MLLMs.