Explore the cutting-edge concept of Test-Time Reinforcement Learning (TTRL) in this 33-minute video that examines how this technology pushes the boundaries of AI self-learning capabilities. Learn how TTRL enables Language Models to reach their maximum self-learning capacity within current AI training methodologies, while also investigating the remaining limitations that affect AI self-rewarding and self-referencing reinforcement learning. Based on research from Tsinghua University and Shanghai AI Lab, this presentation delves into the implications of these advancements for AI self-evolution and what they might mean for the future of artificial intelligence development.
Overview
Syllabus
CODE RED: TTRL Unlocks AI Self-Evolution
Taught by
Discover AI
Related Courses
-
No AI Self-Improvement with Reinforcement Learning - Limitations of RLVR
-
The History and Relevance of the Rise of Generative AI
-
Deep Dive Into Self-Rewarding Language Models - Training Models as Their Own Judges
-
AI Agent Self-Improvement and Self-Fine-Tuning Through Reinforcement Learning
-
AI Mathematics Explained - The Easy Way: Reinforcement Learning and Multi-Agent Systems
-
Scaling RL: 3B AI with Long Chain-of-Thought and 4 Patterns
