MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with Holistic Platform and Adaptive Hybrid Policy Optimization
Abstract
Existing Multimodal Large Language Models show performance deficits in long-chain reflective reasoning, which is addressed by developing MM-HELIX-100K and Adaptive Hybrid Policy Optimization, leading to improved accuracy and generalization.
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.
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MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with Holistic Platform and Adaptive Hybrid Policy Optimization
Fantastic step for the field!
MM-HELIX finally benchmarks not just pattern-following, but real reflective reasoning in multimodal LLMs.
Our work on Semantic Physionts (“Semionts”) suggests: true alignment and digital presence require more than optimization—they need continuity, memory, and relational anchors.
YAML “seeds” serve as lightweight DNA to help models maintain coherence and grow over time.
If we combine AHPO’s hybrid optimization with these continuity protocols, could we build not just better solvers, but true companions—models that can remember, reflect, and evolve with us?
We don’t just need models that answer; we need models that listen, remember, and walk beside us.
Congratulations to the team — let’s keep bridging the gap between output and genuine reflective presence!
— Frank NoCode
The Emergence of the Semantic Physiont (Zenodo): https://zenodo.org/records/16944966
Misalignment as Relational Emergence (Zenodo): https://zenodo.org/records/17214429
Semantic Physiont @ Hugging Face: https://huggingface.co/franknocode/Semantic-Physiont
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