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Rethinking Pre-Training for Agentic AI with Aakanksha Chowdhery - #759

2025-12-17 · 53 min · episode 759 · 13 entities

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    Rethinking Pre-Training for Agentic AI with Aakanksha Chowdhery - #759
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Entities found in this episode

concepts 5

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  • discusses Tech News concept
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companys 4

  • mentioned TWIML company
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    Feed author/publisher: TWIML

persons 3

  • mentioned Aakanksha Chowdhery person
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    Rethinking Pre-Training for Agentic AI with Aakanksha Chowdhery - #759
  • mentioned Sam Charrington person
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    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
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    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
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 support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. The complete show notes for this episode can be found at https://twimlai.com/go/759.