3 citations · 4 across the 10 of their papers we have counts for
10 papers
Intrinsic Mutual Information as a Modulator for Preference Optimization
Peng Liao, Peijia Zheng, Lingbo Li +2
Offline preference optimization methods, such as Direct Preference Optimization (DPO), offer significant advantages in aligning Large Language Models (LLMs) with human values. Howe…
Accelerating Scientific Research with Gemini: Case Studies and Common Techniques
David P. Woodruff, Vincent Cohen-Addad, Lalit Jain +33
Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks,…
CharacterFlywheel: Scaling Iterative Improvement of Engaging and Steerable LLMs in Production
Yixin Nie, Lin Guan, Zhongyao Ma +19
This report presents CharacterFlywheel, an iterative flywheel process for improving large language models (LLMs) in production social chat applications across Instagram, WhatsApp,…
Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents
Zhihan Liu, Lin Guan, Yixin Nie +6
Generalist LLM agents are often post-trained on a narrow set of environments but deployed across far broader, unseen domains. In this work, we investigate the challenge of agentic…
Graph2Eval: Automatic Multimodal Task Generation for Agents via Knowledge Graphs
Yurun Chen, Xavier Hu, Yuhan Liu +8
As multimodal LLM-driven agents advance in autonomy and generalization, traditional static datasets face inherent scalability limitations and are insufficient for fully assessing t…
Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Hangyi Jia, Yuxi Qian, Hanwen Tong +3
Recent advances in large language models (LLMs) have enabled the emergence of general-purpose agents for automating end-to-end machine learning (ML) workflows, including data analy…
SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification
Sasan Tavakkol, Lin Chen, Max Springer +4
Scarcity of labeled data, especially for rare events, hinders training effective machine learning models. This paper proposes SYNAPSE-G (Synthetic Augmentation for Positive Samplin…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
Scaling On-Device GPU Inference for Large Generative Models
Jiuqiang Tang, Raman Sarokin, Ekaterina Ignasheva +5
Driven by the advancements in generative AI, large machine learning models have revolutionized domains such as image processing, audio synthesis, and speech recognition. While serv…
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search
Zujie Liang, Feng Wei, Wujiang Xu +3
Recent advancements in large language models (LLMs) have shown remarkable potential in automating machine learning tasks. However, existing LLM-based agents often struggle with low…