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H$^2$R: Hierarchical Hint-Based Reward for Mathematical Reasoning via Reinforcement Learning

Paper recorded by Signals 4 on 2026-10-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.LG · 机器学习 · first seen 2026-10-30

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 prob

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#124 most recent of 737 cs.LG papers we have recorded · ↑ newer: When to spend privacy: optimal allocation in private learning · ↓ older: Low-Bit GDN Recurrent-State Quantization: Global Gradient-aware Alloca
Cite this page: H$^2$R: Hierarchical Hint-Based Reward for Mathematical Reasoning via Reinforcement Learning: the #124 most recent of 737 cs.LG papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/h-2-r-hierarchical-hint-based-reward-for-mathematical-reasoning-via-reinforcemen.html
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