Filtered to large language models · clear filter
This paper introduces Kimi K3, a large-scale, open-source AI model that achieves state-of-the-art performance on a range of tasks, including vision and coding, and is designed to be more efficient and scalable than previous models, making it a promising candidate for real-world applications.
This paper introduces RAGU, an open-source GraphRAG engine that improves large language models with structured knowledge by separating extraction and consolidation, and trains a compact extractor that outperforms larger models on knowledge-graph construction and GraphRAG tasks. Practitioners might care because RAGU can efficiently generate more accurate and complete context for tasks like factoid-level evidence recall and multi-hop question answering.
This paper introduces a new method to improve large language models (LLMs) called RLSVR, which uses a task-transformation technique to generate self-verifiable rewards, enabling LLM self-improvement on open-ended tasks. Practitioners might care about this because it could lead to more reliable and scalable self-improvement methods for LLMs.
This paper introduces a method called DecoEvo, which helps large language models improve by co-evolving a solver skill and a rubric-generator skill in a way that's more efficient and effective. Practitioners might care about this because it could lead to better performance and more reliable optimization in open-ended tasks.
This paper introduces a benchmark to measure how well large language models (LLMs) can prepare training data, and how well they evaluate the quality of that data. Practitioners might care because improving data preparation can lead to better model performance.
This paper develops a large audio language model that can answer questions with specific timestamps, improving its ability to understand long audio recordings. Practitioners in audio and speech recognition may care about this development as it enables more accurate and context-specific information retrieval from audio data.
This paper proposes a new method for reinforcement learning in large language models, called Group Entropy-Controlled Policy Optimization (GEPO), which helps balance exploration and exploitation by controlling entropy levels across different tasks. Practitioners might care about GEPO because it can lead to more balanced and task-specific exploration levels.
This paper investigates using hypernetworks for large-scale knowledge injection into language models, a technique that can improve their ability to answer factual questions. Practitioners may care because it could lead to more accurate and scalable language models for applications like customer service or question-answering systems.
This paper explores using large language models to improve execution costs in algorithmic trading by breaking down a large order into smaller ones, and finds that these models can outperform human traders and other approaches in certain situations.
This paper improves a type of reinforcement learning (RL) called rubric-based RL, which helps large language models (LLMs) perform well on open-ended tasks. A practitioner might care about this paper because it addresses a common problem in RL, where some criteria (or rules) are not explored properly, and it shows that its new method can improve performance on these tasks.
This paper proposes a method to generate synthetic data for training API-calling agents without the need for an actual environment. This is useful for scalability, as collecting high-quality data at scale can be difficult. Practitioners might care about this approach because it could speed up the development of AI agents that can interact with APIs.
This paper proposes a new way for large language models to learn from feedback, allowing them to retain more detailed information about the quality of their responses and learn from it in a more nuanced way. Practitioners might care because this approach could lead to better performance on tasks where the model doesn't have a clear way to evaluate its own output.
This paper proposes a new method to improve reinforcement learning for large language models by using a predictive divergence mask, which helps stabilize off-policy updates. Practitioners caring about improving the stability and performance of LLM-based reinforcement learning might find this approach useful.
This paper develops a new method to restore illegible historical documents by using large language models with external knowledge to fill in missing information, which can be useful for experts who need to analyze these documents quickly.
This paper proposes a new method called Distilled Reinforcement Learning that improves large language model post-training by providing fine-grained guidance to transfer new knowledge from a teacher model to a student model. Practitioners might care because it outperforms standard reinforcement learning and on-policy distillation methods in terms of knowledge transfer and model performance.
This paper creates a systematic framework for understanding memory in large language models, helping researchers design more scalable and adaptive language models. Practitioners might care because it provides a unified perspective on memory mechanisms, making it easier to evaluate and improve language model performance.
This paper improves the Transformer model by introducing Multi-Head Attention Residuals, which allows each sublayer to attend to different parts of the model's history, reducing the impact of disagreements between different feature subspaces. Practitioners might care because this could lead to better performance in large models.