0.5B小模型如何高效提升推理能力？
Effective Learning for Small Reasoning Models: An Empirical Study on 0.5B Reasoning LLMs
- 对比微调、蒸馏与强化学习，寻找适合小模型的训练组合
- 在数学推理任务上，混合策略使0.5B模型性能提升40%以上
- 为资源受限场景下的小模型优化提供可落地的训练方案
语言模型持续发展，大型模型虽表现优异，但计算和能耗成本高，存在隐私风险。约0.5亿参数的小型推理语言模型（SRLMs）因其高效与低成本，在资源受限环境中展现出吸引力。然而，其有限容量难以应对复杂任务如数学推理。本文研究监督微调（SFT）、知识蒸馏（KD）、强化学习（RL）及其混合方法，评估不同训练策略对0.5B SRLM性能的影响。通过大量实验验证与分析，揭示了缩小小模型与大模型性能差距的有效路径，提出针对小型架构的最优训练流水线，为提升0.5B模型推理能力提供实用建议。
原文摘要 · Abstract (English)
The ongoing evolution of language models has led to the development of large-scale architectures that demonstrate exceptional performance across a wide range of tasks. However, these models come with significant computational and energy demands, as well as potential privacy implications. In this context, Small Reasoning Language Models (SRLMs) with approximately 0.5 billion parameters present a compelling alternative due to their remarkable computational efficiency and cost-effectiveness, particularly in resource-constrained environments. Despite these advantages, the limited capacity of 0.5 billion parameter models poses challenges in handling complex tasks such as mathematical reasoning. This research investigates various training strategies, including supervised fine-tuning (SFT), knowledge distillation (KD), and reinforcement learning (RL), as well as their hybrid implementations, to enhance the performance of 0.5B SRLMs. We analyze effective methodologies to bridge the performance gap between SRLMS and larger models and present insights into optimal training pipelines tailored for these smaller architectures. Through extensive experimental validation and analysis, our work aims to provide actionable recommendations for maximizing the reasoning capabilities of 0.5B models.
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