# Rethinking Pre-Training for Agentic AI [Aakanksha Chowdhery] - 759

Source: https://www.youtube.com/watch?v=jflz6jHMaMc
Recap page: https://rapidrecap.app/video/jflz6jHMaMc
Generated: 2025-12-17T20:06:49.218+00:00

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## Quick Overview

Aakanksha Chowdhery argues that pre-training for agentic AI needs a fundamental rethink, moving beyond static benchmarks like GLUE and SuperGLUE to focus on capabilities like multi-step reasoning, tool use, and long-context retrieval, which are essential for building truly useful agents that can learn from interaction and recover from failure.

**Key Points:**
- Pre-training for agentic AI requires moving beyond static benchmarks like GLUE and SuperGLUE to focus on dynamic capabilities necessary for agents interacting with environments.
- Key capabilities for future agentic models include multi-step reasoning, tool use, and long-context retrieval over millions of tokens.
- Chowdhery notes that while current LLMs are good at next-token prediction, they often fail at reasoning across multiple steps or correctly identifying which parts of the data matter.
- The current industry focus on massive scale (e.g., 540 billion parameters for PaLM) is becoming costly, and the evaluation benchmarks need to evolve to reflect real-world agentic tasks.
- Reflection AI's mission is to build frontier open-ended intelligence, focusing on models that can reason and learn from interaction, rather than just static data.
- The work involves creating benchmarks that test for these specific agentic skills, such as planning, tool use, and self-correction after failure.

![Screenshot at 00:00: Aakanksha Chowdhery introducing the topic of rethinking pre-training for agentic AI on the TWiML AI Podcast.](https://ss.rapidrecap.app/screens/jflz6jHMaMc/00-00-00.png)

**Context:** Aakanksha Chowdhery, a Researcher at Reflection AI, joins Sam Charrington on the TWiML AI Podcast to discuss the necessary evolution of pre-training methodologies for developing truly agentic AI systems. The conversation centers on the limitations of existing large language model (LLM) benchmarks and the need to focus pre-training on skills like reasoning, tool use, and long-context memory to create agents capable of navigating complex, real-world workflows.

## Detailed Analysis

Aakanksha Chowdhery argues that the current paradigm of pre-training large language models (LLMs), which heavily relies on static benchmarks and massive data volumes, is insufficient for developing truly agentic AI. For models to be useful as agents that interact with dynamic environments, pre-training must shift focus from merely next-token prediction toward complex capabilities like multi-step reasoning, tool use, and long-context retrieval. Chowdhery points out that models like PaLM (540 billion parameters) show strong next-token prediction but struggle with fundamental reasoning abilities when tasks require multi-step planning or distinguishing relevant context across long inputs. She emphasizes that current evaluation benchmarks often fail to capture these agentic skills. To address this, Reflection AI focuses on building open-ended intelligence where models can learn from interaction and recover from failures, rather than just memorizing patterns. This requires new evaluation methods that test for planning, tool use, and the ability to correct course after errors, moving beyond simply comparing against existing static benchmarks.

### Rethinking Pre-training Focus

- Moving beyond static benchmarks like GLUE/SuperGLUE to focus on agentic capabilities
- Need for models that learn from interaction and recover from failure
- Current reliance on massive scale (e.g., PaLM) is becoming prohibitively expensive.

### Key Agentic Capabilities

- Future models require strong multi-step reasoning, tool use proficiency, and long-context retrieval abilities.

### Evaluation Challenges

- Existing benchmarks often fail to test for emergent reasoning capabilities or the ability to correctly prioritize relevant context.

### Reflection AI's Approach

- Building frontier open-ended intelligence by focusing on models that can effectively utilize tools and learn from execution feedback.

### Future Benchmarks

- Need for evaluation systems that test reasoning across different modalities (code, text) and complex, multi-step tasks, rather than just next-token prediction accuracy.

![Screenshot at 00:00: Aakanksha Chowdhery introducing the topic of rethinking pre-training for agentic AI on the TWiML AI Podcast.](https://ss.rapidrecap.app/screens/jflz6jHMaMc/00-00-00.png)
![Screenshot at 00:42: Sam Charrington asking about the feasibility of achieving desired model capabilities through current methods.](https://ss.rapidrecap.app/screens/jflz6jHMaMc/00-00-42.png)
![Screenshot at 01:17: Chowdhery discussing the need for models that can interact with environments and reason across multiple steps.](https://ss.rapidrecap.app/screens/jflz6jHMaMc/00-01-17.png)
![Screenshot at 02:27: Chowdhery explaining that the mission of Reflection AI is to build frontier open-ended intelligence.](https://ss.rapidrecap.app/screens/jflz6jHMaMc/00-02-27.png)
![Screenshot at 04:54: Sam Charrington and Aakanksha Chowdhery smiling during the discussion, showing engagement.](https://ss.rapidrecap.app/screens/jflz6jHMaMc/00-04-54.png)