2 citations · 2 across the 6 of their papers we have counts for
9 papers
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
Jinyang Wu, Shuo Yang, Zhengxi Lu +8
Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based rein…
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning
Shuo Yang, Jinyang Wu, Zhengxi Lu +8
Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermedi…
Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles
Jinyang Wu, Guocheng Zhai, Ruihan Jin +7
The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on mo…
Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition
Ran Liu, Fengyu Zhang, Cong Yu +7
This article presents our results for the eighth Affective Behavior Analysis in-the-wild (ABAW) competition.Multimodal emotion recognition (ER) has important applications in affect…
AffectGPT: Dataset and Framework for Explainable Multimodal Emotion Recognition
Zheng Lian, Haiyang Sun, Licai Sun +3
Explainable Multimodal Emotion Recognition (EMER) is an emerging task that aims to achieve reliable and accurate emotion recognition. However, due to the high annotation cost, the…
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
Zheng Lian, Haiyang Sun, Licai Sun +15
Multimodal emotion recognition is an important research topic in artificial intelligence. Over the past few decades, researchers have made remarkable progress by increasing the dat…