Bidirectional Curriculum Generation: A Multi-Agent Framework for Data-Efficient Mathematical Reasoning
#Bidirectional Curriculum Generation #multi-agent framework #data-efficient learning #mathematical reasoning #AI curriculum #adaptive training #problem-solving
📌 Key Takeaways
- A new multi-agent framework called Bidirectional Curriculum Generation improves mathematical reasoning with less data.
- The framework uses bidirectional interaction between agents to create adaptive learning curricula.
- It enhances data efficiency by generating tailored training sequences for complex problem-solving.
- The approach shows promise for advancing AI capabilities in mathematical and logical domains.
📖 Full Retelling
arXiv:2603.05120v1 Announce Type: new
Abstract: Enhancing mathematical reasoning in Large Language Models typically demands massive datasets, yet data efficiency remains a critical bottleneck. While Curriculum Learning attempts to structure this process, standard unidirectional approaches (simple-to-complex) suffer from inefficient sample utilization: they blindly escalate complexity even when foundational gaps persist, leading to wasted computation on unsolvable problems. To maximize the instr
🏷️ Themes
AI Education, Mathematical Reasoning
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--> Computer Science > Artificial Intelligence arXiv:2603.05120 [Submitted on 5 Mar 2026] Title: Bidirectional Curriculum Generation: A Multi-Agent Framework for Data-Efficient Mathematical Reasoning Authors: Boren Hu , Xiao Liu , Boci Peng , Xinping Zhao , Xiaoran Shang , Yun Zhu , Lijun Wu View a PDF of the paper titled Bidirectional Curriculum Generation: A Multi-Agent Framework for Data-Efficient Mathematical Reasoning, by Boren Hu and 6 other authors View PDF HTML Abstract: Enhancing mathematical reasoning in Large Language Models typically demands massive datasets, yet data efficiency remains a critical bottleneck. While Curriculum Learning attempts to structure this process, standard unidirectional approaches (simple-to-complex) suffer from inefficient sample utilization: they blindly escalate complexity even when foundational gaps persist, leading to wasted computation on unsolvable problems. To maximize the instructional value of every training sample, we introduce a novel Bidirectional Curriculum Generation framework. Unlike rigid trajectories, our multi-agent ecosystem mimics adaptive pedagogy to establish a closed feedback loop. It dynamically generates data by either complicating problems to challenge the model or, crucially, simplying them to repair specific reasoning failures. This mechanism ensures that the model consumes only the most effective data at any given stage. Grounded in the Optimal Pacing Theorem, our approach optimizes the learning trajectory, significantly outperforming baselines while achieving superior reasoning performance with substantially fewer instruction samples. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.05120 [cs.AI] (or arXiv:2603.05120v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.05120 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Boren Hu [ view email ] [v1] Thu, 5 Mar 2026 12:49:21 UTC (801 KB) Full-text links: Access Paper: View a PD...
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