aiqbench analysis
This paper is squarely about AI: it proposes a reinforcement learning reward design (H²R) for training LLMs on multi-step math problems, so it passes the topic gate. Its core claim is that structured intermediate hints from sub-question decomposition improve RLVR training, lifting Qwen2.5-Math-7B from 30.4% to 39.1% on the authors' new HiMath benchmark. Readers should weigh these gains as results on a self-introduced benchmark, with the Oracle-hint ceiling (45.6%) showing how much headroom depends on hint quality.
H$^2$R: Hierarchical Hint-Based Reward for Mathematical Reasoning via Reinforcement Learning
arXiv:2610.39934v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards has shown great promise in enhancing mathematical reasoning in large language models. However, existing approaches provide limited intermediate supervision for complex, multi-step reasoning tasks. To bridge this gap, we propose \textbf{Hierarchical Hint-based Reward}, a framework that leverages the intrinsic sub-question structure of mathematical problems as structured intermediate conditioning signals during RLVR training. Unlike chain-of-thought prompting, these hierarchical hints serve as logically grounded intermediate milestones that effectively reduce the reasoning horizon toward the final answer. To validate this framework, we introduce \textbf{HiMath}, a benchmark of hierarchically structured mathematical problems. Across diverse LLMs, hierarchical hints consistently improve reasoning performance even without task-specific training. When integrated into RL training via H$^2$R, they boost Qwen2.5-Math-7B accuracy on HiMath from 30.4\% to 39.1\% using hierarchical hints, and up to 45.6\% with Oracle Hierarchical Hints. This pronounced improvement demonstrates that incorporating reliable intermediate checkpoints into reward design is essential for sustaining complex mathematical reasoning.
Abstract:Reinforcement Learning with Verifiable Rewards has shown great promise in enhancing mathematical reasoning in large language models. However, existing approaches provide limited intermediate supervision for complex, multi-step reasoning tasks. To bridge this gap, we propose \textbf{Hierarchical Hint-based Reward}, a framework that leverages the intrinsic sub-question structure of mathematical problems as structured intermediate conditioning signals during RLVR training. Unlike chain-of-thought prompting, these hierarchical hints serve as logically grounded intermediate milestones that effectively reduce the reasoning horizon toward the final answer. To validate this framework, we introduce \textbf{HiMath}, a benchmark of hierarchically structured mathematical problems. Across diverse LLMs, hierarchical hints consistently improve reasoning performance even without task-specific training. When integrated into RL training via H$^2$R, they boost Qwen2.5-Math-7B accuracy on HiMath from 30.4\% to 39.1\% using hierarchical hints, and up to 45.6\% with Oracle Hierarchical Hints. This pronounced improvement demonstrates that incorporating reliable intermediate checkpoints into reward design is essential for sustaining complex mathematical reasoning.
Machine Learning (cs.LG)
arXiv:2610.39934 [cs.LG]
(or arXiv:2610.39934v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.39934 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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