6 papers
One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
Yuan Pu, Yazhe Niu, Jia Tang +3
In heterogeneous multi-task decision-making, tasks not only exhibit diverse observation and action spaces but also vary substantially in their underlying complexities. While conven…
HH-Codec: High Compression High-fidelity Discrete Neural Codec for Spoken Language Modeling
Rongkun Xue, Yazhe Niu, Shuai Hu +3
Discrete speech tokenization is a fundamental component in speech codecs. However, in large-scale speech-to-speech systems, the complexity of parallel streams from multiple quantiz…
CleanS2S: Single-file Framework for Proactive Speech-to-Speech Interaction
Yudong Lu, Yazhe Niu, Shuai Hu +1
CleanS2S is a framework for human-like speech-to-speech interaction that advances conversational AI through single-file implementation and proactive dialogue capabilities. Our syst…
Empowering LLMs in Decision Games through Algorithmic Data Synthesis
Haolin Wang, Xueyan Li, Yazhe Niu +2
Large Language Models (LLMs) have exhibited impressive capabilities across numerous domains, yet they often struggle with complex reasoning and decision-making tasks. Decision-maki…
PsyDI: Towards a Personalized and Progressively In-depth Chatbot for Psychological Measurements
Xueyan Li, Xinyan Chen, Yazhe Niu +2
In the field of psychology, traditional assessment methods, such as standardized scales, are frequently critiqued for their static nature, lack of personalization, and reduced part…
ReZero: Boosting MCTS-based Algorithms by Backward-view and Entire-buffer Reanalyze
Chunyu Xuan, Yazhe Niu, Yuan Pu +3
Monte Carlo Tree Search (MCTS)-based algorithms, such as MuZero and its derivatives, have achieved widespread success in various decision-making domains. These algorithms employ th…