Tag: Fine-tuning
All the articles with the tag "Fine-tuning".
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Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging
Published: at 11:35 AM
95.81 🤔本文通过模型融合方法整合快速思维和慢速推理能力，实现长到短推理，在7B模型上将响应长度压缩高达55%且保持性能，提出了一种高效解决大语言模型过度思考问题的方案。
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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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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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Gameplay Highlights Generation
Published: at 11:06 AM 92.19 🤔This paper presents a method to generate gameplay highlight reels by finetuning the X-CLIP multimodal model on an in-house FPS game dataset, achieving over 90% event detection accuracy and demonstrating transfer learning, while optimizing deployment through quantization.
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TL;DR: Too Long, Do Re-weighting for Effcient LLM Reasoning Compression
Published: at 11:22 AM 92.10 🤔本文提出TLDR方法，通过动态再加权系统1和系统2推理数据，显著压缩大型语言模型的推理输出token数量（约40%），同时在多难度数学任务上基本保持准确性。