When you tackle a complex puzzle like Sudoku or a strategy game like chess, what does your thought process look like? You likely don’t find the solution in one perfect, linear sequence of steps. Instead, you hypothesize, test your ideas, hit dead ends, backtrack, and refine your strategy. This cycle of trial, error, and correction—what cognitive scientists call reflective reasoning—is the hallmark of human intelligence. It’s how we solve hard problems.

For all their recent triumphs, Multimodal Large Language Models (MLLMs) like GPT-4o and Gemini still largely lack this fundamental skill. They are masters of the single-pass solution—generating answers in a direct, forward flow. Yet, when faced with a complex, multi-step problem requiring careful planning and self-correction, they often falter. A single mistake early on can derail the entire process, and they have no intrinsic mechanism for realizing this, stepping back, and trying again.

This gap is a major barrier to developing more capable and reliable AI. How can we measure this reflective reasoning skill? And more importantly, how can we teach it?

A new research paper, “MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning”, tackles the challenge head-on. The authors make three pivotal contributions:

  1. MM-HELIX Benchmark: A new, challenging suite of multimodal tasks designed to test an MLLM’s ability to reason, reflect, and backtrack over long chains of thought.
  2. MM-HELIX-100K Dataset: A large-scale, high-quality dataset of 100,000 “reasoning traces” that show a model how to solve these complex problems, complete with reflective steps.
  3. Adaptive Hybrid Policy Optimization (AHPO): A novel training strategy that combines learning from expert examples with self-guided exploration, enabling models to acquire and generalize reflective reasoning without forgetting prior skills.

This article will unpack this fascinating work, exploring how MM-HELIX redefines evaluation and training for AI reasoning. By the end, you’ll understand why reflective reasoning represents the next frontier for MLLMs—and how this paper pushes us closer to achieving it.

An overview of the entire MM-HELIX framework, from the benchmark tasks on the left, to data generation and AHPO training in the middle, to evaluation and generalization on the right.

Figure 1: Overview of the proposed MM-HELIX framework. It introduces a multimodal benchmark, a reflective reasoning dataset, and an adaptive learning strategy to boost and generalize reasoning capability.

The Problem: MLLMs Can’t “Change Their Minds”

Today’s MLLMs are impressively versatile. They can describe images, explain scientific figures, and even generate computer code. But much of this ability boils down to a sophisticated form of pattern completion—predicting the most likely continuation given prior context. This works remarkably well for problems where a direct solution path exists.

However, many real-world tasks demand more. Solving a Minesweeper puzzle or planning an optimal route for a snake in a game like Nibbles requires:

  • Long-Chain Reasoning: The solution involves extended sequences of interdependent steps.
  • State Tracking: Each move alters the problem state—AI must remember and reason over these transitions.
  • Hypothesis and Backtracking: Success often depends on exploring potential solutions, recognizing dead ends, and revising assumptions.

Existing multimodal benchmarks rarely test these skills. They tend to be text-only or simplified (e.g., multiple-choice formats), which don’t require models to generate, revise, and validate full solutions. As a result, reflective reasoning in MLLMs remains underexplored.

MM-HELIX: A New Gauntlet for AI Reasoners

To close this gap, the researchers introduced MM-HELIX, a benchmark that serves as a stress test for an AI model’s capacity for iterative and multimodal reasoning. Each component of MM-HELIX is anchored in four guiding principles: Multimodal, Long-Chain Reasoning, Reflection, and End-to-End Solving.

The benchmark includes 42 diverse tasks, grouped into four domains: Algorithms, Graphs, Puzzles, and Games.

Overview of the 42 tasks included in MM-HELIX, categorized into Puzzles, Graphs, Algorithms, and Games.

Figure 2: The assorted and challenging tasks in MM-HELIX, comprising puzzles, algorithmic problems, graphical reasoning, and games, each with five difficulty levels.

These aren’t academic exam questions—they include problems like Sudoku, Kakuro, and Nonograms, as well as logic games such as Sokoban and Nibbles. Each task demands that the model parse visual content, understand intricate rules, and navigate long reasoning chains that require ongoing self-correction.

To ensure scalability and controlled difficulty, the authors built a procedural generation pipeline. This system can automatically create new instances of each task with tunable parameters, enabling fine-grained comparisons. For instance, it can produce a simple 6×6 snake puzzle (Level 1) with one apple, or a complex 10×10 puzzle (Level 5) with several apples. This systematic scaling reveals precisely when and how a model’s reasoning breaks down.

An example of the Nibbles task at Level 5 difficulty. The model must provide a sequence of moves that eat all apples without colliding with walls or itself.

Figure 3: The Level 5 Nibbles task—solving it requires long-term strategic planning and reflection at every step.

In the Nibbles example, the model must generate a move sequence such as “up left left down…” to collect all apples safely. Each move changes the environment: eating an apple increases the snake length, reducing future maneuverability. The model must plan ahead and reconsider paths—typical reflective reasoning dynamics that even top-tier MLLMs currently struggle to master.

Creating the “Textbook”: The MM-HELIX-100K Dataset

MM-HELIX revealed that even state-of-the-art multimodal language models perform poorly on reflective reasoning. For instance, Qwen-2.5-VL-72B, one of the most powerful open models, achieved only 13.9% accuracy. To address this, the researchers developed a “curriculum” for teaching reflection—the MM-HELIX-100K dataset, comprising 100,000 detailed reasoning trajectories.

Generating reasoning chains for complex multimodal tasks at scale is notoriously difficult. Models prompted from scratch often produce verbose, inconsistent, or logically incorrect reasoning. To overcome this, the authors introduced Step-Elicited Response Generation (SERG), a hybrid pipeline that combines algorithmic logic with large model enhancement.

A diagram of the Step-Elicited Response Generation (SERG) pipeline showing how a rule-based scaffold is enhanced by an LLM and then verified to create high-quality training data.

Figure 4: The SERG pipeline efficiently generates human-like reasoning traces by fusing programmatic scaffolding with LLM-based refinement and automated verification.

The pipeline operates in three steps:

  1. Rule-Based Scaffolding: A deterministic solver constructs a skeletal logical path—mechanically sound but linguistically rigid.
  2. LLM-Based Enhancement: A powerful model (here, Qwen3-235B) enriches this skeleton, adding natural language reflection and context, turning technical reasoning into fluent “think-aloud” chains.
  3. Automated Verification: The enhanced response is validated by an algorithmic verifier to ensure correctness and consistency, filtering out erroneous or incoherent samples.

The result is a large-scale dataset of training examples that pair linguistic nuance with algorithmic validity, bridging logic and language to teach models reflective reasoning.

The Training Regimen: Adaptive Hybrid Policy Optimization (AHPO)

Even with high-quality data, training reflective reasoning remains challenging. The researchers discovered that traditional learning methods break down:

  • Supervised Fine-Tuning (SFT): Directly fine-tuning on MM-HELIX-100K causes catastrophic forgetting. The model learns to solve new puzzles but loses prior general reasoning proficiency.
  • Reinforcement Learning (RL): Pure RL fails when rewards are sparse—complex multimodal tasks yield few successful trajectories, giving the model little feedback to improve.

To reconcile these approaches, they devised Adaptive Hybrid Policy Optimization (AHPO)—a unified training framework that dynamically switches between learning from expert examples and exploring independently.

A schematic of the AHPO training process for a Minesweeper puzzle. The model dynamically switches between learning from expert responses and self-generated reasoning based on reward density.

Figure 5: AHPO dynamically blends supervised and reinforcement learning, allowing the model to leverage expert data when rewards are sparse and explore autonomously when proficient.

Mathematically, AHPO combines two loss functions:

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

Where:

  • Off-Policy Term (\(\mathcal{L}_{\text{off-policy}}\)) — A negative log-likelihood loss guiding the model toward expert trajectories:

    \[ \mathcal{L}_{\text{off-policy}}(\theta) = -\frac{1}{|y^*|} \sum_{t=1}^{|y^*|} \log \pi_\theta(y_t^* \mid x, y_{

  • On-Policy Term (\(\mathcal{L}_{\text{on-policy}}\)) — A policy gradient-based loss encouraging exploration:

    \[ \mathcal{L}_{\text{on-policy}}(\theta) = -\frac{1}{\sum_{i=1}^{N} |\tau_i|} \sum_{i=1}^{N} \sum_{t=1}^{|\tau_i|} \text{CLIP}(r_{i,t}(\theta), A_i, \epsilon) \]

  • Adaptive Coefficient (\(\xi\)) — Turns expert guidance on or off depending on success rate:

    \[ \xi = 1\left(\sum_{i=1}^{N_{\text{on}}} \mathbb{I}(R(\tau_i)=1) < \hat{R}\right) \]

In practice, AHPO keeps “training wheels” on while the model struggles—leveraging expert CoT guidance when success rates are low—and removes them as proficiency grows, encouraging self-driven refinement.

Graphs comparing reward curves: Static-AHPO outperforms GRPO and LUFFY, and adaptive AHPO improves long-term stability. Adaptive AHPO (red) achieves higher rewards than Static-AHPO (blue) over prolonged training iterations.

Figures 6 & 7: Reward comparison between training strategies. Adaptive AHPO demonstrates stable and robust learning versus static or pure RL methods.

The Results: A Leap in Reasoning and Generalization

The evaluation results are telling. The team benchmarked 23 leading MLLMs across the MM-HELIX test set.

Table showing performance of 23 MLLMs on MM-HELIX, highlighting gaps in reflective reasoning and modality differences.

Table 1: Comparative scores on MM-HELIX reveal major deficits in reflective reasoning among current models.

Key findings:

  • Severe Performance Deficit: No open-source model surpassed 34% accuracy. Even GPT-5 reached only 58.1%.
  • Structured vs. Dynamic Weakness: Models excel at algorithmic problems but stumble on rule-heavy, dynamic games.
  • Modality Gap: Text-only tasks yield far higher scores than tasks involving both text and images, underscoring current limits in visual reasoning.

Next, training Qwen2.5-VL-7B using AHPO produced dramatic improvement.

A table comparing AHPO with strategies like SFT, GRPO, and LUFFY. AHPO exhibits the strongest in-domain improvement and generalization.

Table 2: AHPO achieves the largest gains both on MM-HELIX and on unseen math and logic benchmarks, proving generalization.

  • In-Domain Mastery: AHPO pushed MM-HELIX accuracy to 24.9%, a +18.6-point jump over the baseline.
  • Cross-Domain Generalization: Remarkably, these reflective reasoning skills transferred to mathematical and logical benchmarks (MathVision, LogicVista, and others), showing an average +5.7% gain.
  • Avoiding Forgetting: Unlike models trained with SFT alone, AHPO prevents catastrophic forgetting, enabling both specialized learning and general reasoning.

Together, these results prove that reflective reasoning is not merely an abstract concept—it can be practically taught, measured, and transferred across domains.

Conclusion: Teaching AI to Reason Beyond the Surface

MM-HELIX presents a compelling framework for instilling human-like reasoning abilities in AI systems. It systematically diagnoses reasoning shortfalls, constructs clean reflective trajectories, and introduces a training paradigm capable of uniting guidance with exploration.

The key insight from this research is simple yet profound:
Reflective reasoning can be learned and generalized.

By intelligently blending expert demonstration and autonomous discovery, Adaptive Hybrid Policy Optimization equips models to think iteratively—recognizing mistakes, revising their paths, and progressing toward better solutions. This not only enhances their problem-solving capabilities but also makes them more reliable and adaptable in real-world multimodal scenarios.

As MLLMs continue to evolve, approaches like MM-HELIX signal a shift from pattern recognition toward genuine cognitive emulation—AI systems that can reason, reflect, and grow with experience.