MM-HELIX Benchmark

• MM-HELIX Benchmark is a curated evaluation framework designed to assess iterative and reflective reasoning in multimodal large language models.
• It features an automated synthesis pipeline with rule-based generators, deterministic solvers, and automated verifiers to ensure step-by-step reasoning validation.
• The framework employs innovative strategies like SERG and AHPO to enhance training efficiency and generalization across complex multimodal tasks.

MM-HELIX Benchmark is a curated evaluation framework for assessing the long-chain reflective reasoning capabilities of multimodal LLMs (MLLMs). Unlike previous datasets focused on isolated question-answer or short-form reasoning, MM-HELIX targets the ability of models to iteratively solve complex tasks requiring backtracking and self-reflection—a key prerequisite for reliable deployment in domains such as algorithmic problem solving, logic games, and multimodal mathematics. The benchmark comprises a diverse set of synthetic tasks, a rigorously validated scoring methodology, and is complemented by a large-scale data generation and adaptive policy optimization regime to empirically advance reflective reasoning in MLLMs (Zhao et al., 9 Oct 2025).

1. Benchmark Construction

MM-HELIX was constructed using an automated data synthesis engine that programmatically generates multimodal tasks characterized by iterative reasoning requirements. The synthesis pipeline consists of three core components:

• Rule-based code generator: Specifies multimodal question templates adhering to problem-specific rules, with tunable parameters controlling complexity across five levels ("very easy" to "very hard").
• Deterministic Solver: Implements task-specific algorithms to compute ground-truth solutions, supporting reproducible and deterministic benchmarking.
• Automated Verifier: Validates model responses via either direct exact-match (for simple answer types) or multi-step simulation (for solutions involving intermediate steps).

The resulting benchmark contains 1,260 carefully-balanced samples covering 42 synthetic tasks. The task suite spans multiple domains, including Algorithms, Graphs, Puzzles, and Games. Each instance is meticulously annotated for stepwise reasoning validation, ensuring accurate scoring of iterative and backtracking behaviors.

2. Performance Evaluation and Baseline Findings

Extensive empirical analysis on MM-HELIX revealed that state-of-the-art MLLMs display pronounced deficits in long-chain reflective reasoning. For example, Qwen-2.5-VL–72B, a leading proprietary multimodal model, attained only approximately 13.9% accuracy on these tasks, evidencing a substantial gap despite high performance in conventional reasoning. Comparative results further show that models equipped to generate intermediate reflection steps—sometimes referred to as "built-in thinking"—consistently outperform those reliant on direct answer prediction. This highlights an urgent need for methods that instill stepwise reasoning and self-correction, especially in the presence of multimodal content.

3. Step-Elicited Response Generation (SERG) and Dataset Expansion

Addressing the intrinsic challenge of sparse supervision in long-chain tasks, MM-HELIX introduced the Step-Elicited Response Generation (SERG) pipeline to scale up reflective reasoning data:

• Rule-based CoT construction: Programmatically assembles chain-of-thought skeletons using problem-specific anchors denoting key intermediate steps.
• LLM refinement: A powerful model (Qwen3-235B) expands skeletons to produce complete, naturalistic reasoning traces.
• Quality assurance: Each trajectory is filtered by automated validators for accuracy and reasoning diversity.

Through this pipeline, MM-HELIX-100K was created—a dataset of 100,000 high-fidelity reflective CoT traces encompassing all task types and difficulty tiers. The data is then used for instruction-tuning, systematically exposing models to explicit reasoning steps and fostering the development of robust long-chain generation policies.

4. Adaptive Hybrid Policy Optimization (AHPO)

MM-HELIX advances optimization methods via the Adaptive Hybrid Policy Optimization (AHPO) strategy. Standard reinforcement learning (RL) approaches frequently fail on complex reasoning tasks due to two challenges: reward sparsity (where high-level correctness signals are infrequent) and catastrophic forgetting post-supervised fine-tuning. AHPO unifies offline expert supervision and on-policy RL via a dynamic loss modulation schema:

• Loss terms:
  • Offline supervised (expert) loss:

  $$\mathcal{L}_{\text{off-policy}}(\theta) = -\frac{1}{|\mathbf{y}^*|} \sum_t \log \pi_\theta(y_t^*|x, y_{<t}^*)$$ - On-policy clipped policy gradient loss:

  $$\mathcal{L}_{\text{on-policy}}(\theta) = -\frac{1}{\sum_i|\tau_i|} \sum_i \sum_t \mathrm{CLIP}(r_{i,t}(\theta), A_i, \epsilon)$$ - Unified AHPO loss:

  $$\mathcal{L}_{\text{AHPO}}(\theta) = \xi \cdot \mathcal{L}_{\text{off-policy}}(\theta) + \mathcal{L}_{\text{on-policy}}(\theta)$$

  with

  $$\xi = \mathbb{1}\left(\sum_{i=1}^{n} I(R(\tau_i) = 1) < \hat{R}\right)$$

  where $\hat{R}$ is a threshold for successful trajectories.

• Dynamic modulation: As on-policy performance rises (i.e., the model's success rate surpasses $\hat{R}$), the algorithm attenuates the expert loss and favors independent on-policy exploration.

AHPO thus ensures dense learning signals when needed, while allowing the model to autonomously discover improved trajectories once proficient.

5. Empirical Improvements and Generalization

Applying AHPO and MM-HELIX-100K data for instruction-tuning yielded pronounced accuracy gains:

• On the MM-HELIX benchmark, accuracy of Qwen2.5-VL-7B improved by +18.6 percentage points.
• Reflective reasoning capabilities generalized to broader mathematics and logic suites, with an average gain of +5.7 percentage points on out-of-domain tasks.

The results demonstrate both in-domain mastery and generalization, supported by numerical tables (see paper) and detailed ablation analysis.

6. Significance, Implications, and Future Directions

The MM-HELIX framework establishes reflective reasoning in MLLMs as a learnable and transferable skill. By systematizing data generation (SERG), optimizing training via AHPO, and empirically verifying generalization, MM-HELIX provides a foundation for future research on complex multimodal reasoning. A plausible implication is that advanced training regimes combining dense expert signals and on-policy refinement may extend MLLM capabilities toward reliable problem-solving in science, engineering, and advanced mathematical domains. Continued development of benchmarks and policy optimization strategies targeting long-chain reasoning is likely to shape the evolution of general-purpose multimodal intelligence.