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 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.

🗒️ For the full list of resources for this episode, visit the show notes page: https://twimlai.com/go/759.

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📖 CHAPTERS
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00:00 – Introduction
02:26 – Reflection
04:54 – Limitations of post-training for building agents
07:31 – 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 data source
24:11 – Overcoming challenges in training on synthetic data
25:47 – Benchmarks
30:44 – Scaling laws in large models versus small models
33:20 – Long-form versus short-form reasoning
37:57 – Agent’s ability to recover from failure
40:15 – Hallucinations and failure recovery
43:53 – Tool use in agents
46:38 – Coding agents
48:37 – How researchers can contribute to agentic AI

🔗 LINKS & RESOURCES
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Reflection AI – https://reflection.ai/
Reflection AI, an A.I. Model Start-Up, Raises $2 Billion – https://www.nytimes.com/2025/10/09/business/dealbook/reflection-ai-2-billion-funding.html

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