MathForge Framework Improves Mathematical Reasoning by Targeting Harder Questions

Global: MathForge Framework Improves Mathematical Reasoning by Targeting Harder Questions

Researchers from the AMAP-ML group announced on arXiv that they have developed a new framework, MathForge, designed to enhance mathematical reasoning in large reinforcement‑learning models. The study, posted in January 2026, identifies a systematic neglect of difficult questions in current training pipelines and proposes a combined algorithmic and data‑centric solution.

Algorithmic Imbalance in Existing Methods

According to the authors, the widely adopted Group Relative Policy Optimization (GRPO) algorithm unintentionally favors easier questions because the magnitude of policy updates diminishes as question difficulty rises. This hidden bias limits the model’s ability to learn from challenging problem sets.

Difficulty‑Aware Group Policy Optimization

The proposed Difficulty‑Aware Group Policy Optimization (DGPO) corrects this imbalance by introducing difficulty‑balanced group advantage estimates and applying question‑level weighting that prioritizes harder items. By recalibrating the update dynamics, DGPO aims to allocate learning capacity more equitably across the difficulty spectrum.

Multi‑Aspect Question Reformulation

On the data side, the authors note that most augmentation techniques merely rephrase existing questions, leaving intrinsic difficulty unchanged. Their Multi‑Aspect Question Reformulation (MQR) strategy systematically modifies questions across several dimensions—such as logical structure, variable representation, and contextual framing—to raise difficulty while preserving the original correct answer.

Synergistic Training Loop

MathForge creates a feedback loop in which MQR expands the pool of challenging examples, and DGPO learns from this enriched dataset. The authors argue that the two components reinforce each other, leading to more robust reasoning capabilities.

Experimental Validation

Extensive experiments reported in the abstract indicate that MathForge outperforms prior state‑of‑the‑art methods on multiple mathematical reasoning benchmarks. The performance gains are attributed to both the algorithmic adjustments and the harder, diversified question set.

Future Directions

The team has released the code and augmented datasets on GitHub, inviting further exploration of difficulty‑aware training regimes. They suggest that extending the framework to other domains of logical reasoning could be a promising avenue for subsequent research.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.