Tag: Fine-tuning
All the articles with the tag "Fine-tuning".
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Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning
Published: at 11:25 AM
92.52 🤔本文通过理论分析和Re-distillation技术，揭示了小规模SFT在R1风格RL中的效率瓶颈，并以极少样本（<1K）在K&K和MATH数据集上接近RL性能，显著提升了数据效率。
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Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models
Published: at 11:19 AM 85.69 🤔本文研究了口语语言模型（SLM）端到端训练中的灾难性遗忘问题，通过评估模型合并、LoRA缩放因子折扣和经验回放三种策略，发现经验回放最为有效，且结合其他方法可进一步提升性能。
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The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs
Published: at 11:24 AM 92.28 🤔本文通过模块化方法，利用大型语言模型参数在数学推理和多语言能力上的分离性，提出Layer-Swapping等策略，在低资源语言跨语言迁移中显著优于非模块化基线，尤其在数据受限场景下表现最佳。
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Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning
Published: at 11:16 AM 87.82 🤔本文通过实验验证了长上下文能力与推理性能的正相关，提出在监督微调前增强长上下文能力的训练策略，并在数学推理基准上显著提升了模型性能。
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LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging
Published: at 11:22 AM 91.54 🤔本文提出LORE-MERGING框架，通过低秩估计构建近似基础模型和任务向量，无需访问原始基础模型即可实现模型合并，并在多个基准数据集上展现出优于传统方法的性能。