SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization
arXiv:2505.11166v3 Announce Type: replace-cross Abstract: Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-world long-context information, primarily due to insufficient long-context alignment caused by data quality issues, training inefficiencies, and the lack of well-designed optimization objectives. To address these limitations, we propose a framework named \textbf{S}h\textbf{o}rt-to-\textbf{Lo}ng \textbf{P}reference \textbf{O}ptimization (\textbf{SoLoPO}), decoupling long-context preference optimiza
The paper addresses a critical current limitation in LLMs, their effective long-context understanding, which is a major bottleneck for advanced AI applications.
Improving LLMs' ability to process and utilize long-context information is crucial for developing truly capable AI agents and enhancing various white-collar workflows.
This research provides a new methodology for optimizing LLMs for long contexts, potentially leading to more reliable and powerful AI models that can handle complex, real-world data.
- · AI developers
- · Enterprises deploying LLMs
- · AI research institutions
- · LLMs with poor long-context capabilities
- · Applications requiring extensive human data curation
LLMs can better understand and generate coherent responses based on extensive documents or conversational histories.
This improved capability enables more sophisticated AI agents capable of automating complex tasks requiring deep contextual understanding.
The enhanced performance of agentic AI systems could further accelerate the 'AI agents' narrative, potentially disrupting knowledge-worker sectors more rapidly.
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Read at arXiv cs.AI