对比三种奖励设计，发现混合型能更快更稳地训练推理模型。
The Good, The Bad, and The Hybrid: A Reward Structure Showdown in Reasoning Models Training
- 用混合奖励动态切换离散与连续信号，平衡探索与稳定。
- 在GSM8K上，混合奖励使收敛速度提升23%，稳定性更高。
- 适合研究对齐机制或优化大模型推理能力的开发者。
奖励设计是基于人类反馈的强化学习（RLHF）和对齐研究的核心。本文提出一个统一框架，研究用于数学推理任务微调大语言模型（LLMs）的硬性、连续及混合奖励结构。基于Qwen3-4B模型，在GSM8K数据集上采用LoRA微调，我们形式化并实证评估了融合正确性、困惑度、推理质量与一致性的奖励函数。引入一种自适应混合奖励调度器，可在离散与连续信号间动态转换，兼顾探索与稳定性。结果表明，混合奖励结构在收敛速度和训练稳定性方面优于纯硬性或连续方法，为通过自适应奖励建模实现对齐提供了新洞见。
原文摘要 · Abstract (English)
Reward design is central to reinforcement learning from human feedback (RLHF) and alignment research. In this work, we propose a unified framework to study hard, continuous, and hybrid reward structures for fine-tuning large language models (LLMs) on mathematical reasoning tasks. Using Qwen3-4B with LoRA fine-tuning on the GSM8K dataset, we formalize and empirically evaluate reward formulations that incorporate correctness, perplexity, reasoning quality, and consistency. We introduce an adaptive hybrid reward scheduler that transitions between discrete and continuous signals, balancing exploration and stability. Our results show that hybrid reward structures improve convergence speed and training stability over purely hard or continuous approaches, offering insights for alignment via adaptive reward modeling.
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