New Generator Aligns Synthetic Reasoning Problems with Solver Ability

Global: New Generator Aligns Synthetic Reasoning Problems with Solver Ability

A team of AI researchers has introduced a novel problem‑generator framework that explicitly reasons about problem direction before synthesis, aiming to produce training data that matches the competence of large reasoning models. The work appears in the latest version of a preprint posted on arXiv, and it targets the persistent difficulty of creating high‑quality, solver‑aware synthetic datasets for both language and vision‑language tasks.

Motivation and Challenges

Current data‑synthesis pipelines often generate problems indiscriminately, ignoring the solver’s current ability and resulting in low‑value examples, or they rely on intricate balancing mechanisms that are difficult to scale. Moreover, many generators lack intrinsic reasoning, leading to shallow variations that do not adequately challenge advanced models.

Reasoned Problem Generation

The proposed system constructs related problem pairs and enriches them with intermediate, chain‑of‑thought (CoT) designs produced by a dedicated reasoning model. These intermediate steps serve as explicit design strategies that the generator can bootstrap, allowing it to plan problem trajectories that are logically coherent and pedagogically progressive.

Adaptive Difficulty via Solver Feedback

After synthesis, the generator presents each problem to a target solver and treats the solver’s performance as a reward signal. By interpreting this feedback, the system dynamically calibrates difficulty, steering the creation of complementary problems that sit near the edge of the solver’s competence.

Empirical Evaluation

Extensive experiments across ten mathematical and general‑reasoning benchmarks demonstrate an average cumulative improvement of 3.4% over baseline data‑generation methods. The gains hold for both pure language models and multimodal vision‑language models, indicating robust generalization.

Potential Impact

If adopted broadly, this approach could reduce reliance on costly human‑curated datasets, accelerate the training of more capable reasoning systems, and provide a scalable pathway for continual model improvement as solvers evolve.

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.