Rethinking Pre-Training for Agentic AI [Aakanksha Chowdhery] - 759
Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to s…
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Chapters (11)
Introduction
2:26
Reflection
4:54
Limitations of post-training for building agents
7:31
Rethinking pre-training in agents
10:51
Scaling
11:27
Evolving attention mechanisms for agentic capabilities
12:39
Memory as a tool
14:13
Loss objectives and training data
15:50
Fine-tuning loss in agent performance
19:37
Training data
21:29
Augmenting dominant training da
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