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# Lessons from Aakanksha Chowdhery
- URL: https://www.antoinebuteau.com/lessons-from-aakanksha-chowdhery/
- Published: 2026-06-26T05:51:35.000Z
- Updated: 2026-07-18T21:48:07.000Z
- Description: Aakanksha Chowdhery is an AI researcher who led training for Google’s PaLM model and contributed to PaLM-E. Her path from distributed network infrastructure to reinforcement learning for autonomous coding agents reveals the engineering range behind advanced AI systems.
- Author: Antoine Buteau
- Tags: Profile, AI & Machine Learning Profiles

![Visual summary of operating lessons from Aakanksha Chowdhery.](https://www.antoinebuteau.com/content/images/2026/06/lessons-from-aakanksha-chowdhery-profile-infographic.webp)

## Lessons from Aakanksha Chowdhery

Aakanksha Chowdhery is an AI researcher who led the training for Google's 540-billion parameter PaLM model and contributed to multimodal systems like PaLM-E. This profile tracks her career from early work on distributed network infrastructure to her current focus at Reflection AI, where she develops reinforcement learning techniques for autonomous coding agents.

### Part 1: Scaling Large Language Models

1. **On Parameter Growth:** "Scaling language models beyond half a trillion parameters requires careful management of training stability and data pipeline efficiency." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
2. **On Compute Allocation:** "Training massive models efficiently demands distributing workloads across thousands of TPU chips while minimizing communication overhead." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
3. **On Few-Shot Learning:** "Large-scale pre-training allows models to perform well on new tasks with only a few examples, changing how we approach task-specific tuning." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
4. **On Emergent Capabilities:** "As model scale increases, we observe unexpected reasoning capabilities that smaller architectures do not exhibit." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
5. **On Data Quality:** "The composition and filtering of the pre-training dataset are equally important as the parameter count in determining final model performance." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
6. **On Evaluation Metrics:** "Standard benchmarks often fail to capture the full reasoning depth of large models, forcing the development of more complex reasoning tests." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
7. **On Training Instabilities:** "Hardware failures and loss spikes are inevitable at the scale of thousands of accelerators; engineering for fault tolerance is a primary concern." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
8. **On Hardware Utilization:** "Maximizing the Model Flops Utilization is a core metric for ensuring that the underlying hardware is actively computing rather than waiting for data." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
9. **On Memorization vs Generalization:** "Balancing the model capacity to store facts against its ability to generalize rules requires careful tuning of the training duration and data diversity." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
10. **On Post-Training Constraints:** "Relying heavily on post-training to instill reasoning is less effective than baking structural reasoning capabilities into the pre-training phase." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*

### Part 2: The PaLM and Pathways Architecture

1. **On Pathways Infrastructure:** "The Pathways system allows a single model to be trained across multiple TPU Pods asynchronously, breaking traditional hardware boundaries." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
2. **On Client-Server Architecture:** "Separating the training loop into client and server components allows the compute nodes to operate independently of the data feeding processes." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
3. **On Asynchronous Data Fetching:** "Hiding the latency of data retrieval is necessary to keep the accelerators continuously fed during a large-scale training run." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
4. **On Checkpoint Recovery:** "When a node fails, the system must quickly resume from the last checkpoint without restarting the entire cluster." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
5. **On Model Parallelism:** "Splitting the neural network layers across different chips requires optimizing the communication bandwidth between them." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
6. **On Data Parallelism:** "Replicating the model weights across multiple nodes allows the system to process larger batch sizes simultaneously." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
7. **On Network Topology:** "The physical layout and connection speeds between TPU pods dictate the upper limits of how efficiently a model can be trained." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*
8. **On Activation Sharding:** "Memory constraints require sharding the intermediate activation states across different accelerators to fit the entire model into memory." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
9. **On Infrastructure Resilience:** "A training run spanning weeks will inevitably encounter hardware errors, requiring the software stack to handle these transparently." — [*Source: \[PaLM: Scaling Language Modeling with Pathways*](https://arxiv.org/abs/2204.02311?ref=antoinebuteau.com)*\]*

### Part 3: Multimodal AI and Gemini

1. **On Native Multimodality:** "Training a model from the start on image, audio, and text data yields better cross-modal reasoning than stitching together separate models later." — [*Source: \[Gemini: A Family of Highly Capable Multimodal Models*](https://arxiv.org/abs/2312.11805?ref=antoinebuteau.com)*\]*
2. **On Vision-Language Tasks:** "A truly multimodal architecture can answer questions about an image based on the spatial relationships of the objects within it." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
3. **On Audio Integration:** "Processing audio directly as raw waveforms allows the model to capture nuances like tone and speaker identity that text transcripts miss." — [*Source: \[Gemini: A Family of Highly Capable Multimodal Models*](https://arxiv.org/abs/2312.11805?ref=antoinebuteau.com)*\]*
4. **On Multimodal Benchmarks:** "Evaluating models on combined text-and-vision tasks like chart understanding requires different metrics than standard text benchmarks." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
5. **On Data Alignment:** "Ensuring that the text descriptions accurately match the visual content in the training data is difficult but required for grounding." — [*Source: \[Gemini: A Family of Highly Capable Multimodal Models*](https://arxiv.org/abs/2312.11805?ref=antoinebuteau.com)*\]*
6. **On Model Families:** "Creating a family of models from Nano to Ultra allows the same underlying architecture to run on mobile devices and large data centers." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
7. **On Interleaved Data:** "Training on documents where images and text are interleaved naturally teaches the model about document structure and context." — [*Source: \[Gemini: A Family of Highly Capable Multimodal Models*](https://arxiv.org/abs/2312.11805?ref=antoinebuteau.com)*\]*
8. **On Video Understanding:** "Processing video requires the model to understand temporal dependencies and track objects across multiple frames." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
9. **On Cross-Lingual Vision:** "A globally trained multimodal model can interpret text within an image regardless of the language it is written in." — [*Source: \[Gemini: A Family of Highly Capable Multimodal Models*](https://arxiv.org/abs/2312.11805?ref=antoinebuteau.com)*\]*
10. **On Efficiency in Multimodality:** "Encoding high-resolution images efficiently into the model context window requires specialized tokenization strategies." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*

### Part 4: Embodied AI and Robotics

1. **On Embodied Reasoning:** "Language models can act as the reasoning engine for a robot, translating high-level text commands into step-by-step physical actions." — [*Source: \[PaLM-E: An Embodied Multimodal Language Model*](https://arxiv.org/abs/2303.03378?ref=antoinebuteau.com)*\]*
2. **On Sensor Integration:** "Injecting raw sensor data directly into the language model embedding space gives it real-time awareness of the robot environment." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
3. **On Continuous Observation:** "An embodied agent must process continuous visual observations to update its plan when the environment changes unexpectedly." — [*Source: \[PaLM-E: An Embodied Multimodal Language Model*](https://arxiv.org/abs/2303.03378?ref=antoinebuteau.com)*\]*
4. **On Transfer Learning in Robotics:** "Knowledge gained from internet-scale text data can help a robot understand concepts it has never physically interacted with." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
5. **On Action Output:** "The model must output low-level control commands or waypoints that the robot hardware can execute directly." — [*Source: \[PaLM-E: An Embodied Multimodal Language Model*](https://arxiv.org/abs/2303.03378?ref=antoinebuteau.com)*\]*
6. **On Spatial Awareness:** "Understanding physical space from camera feeds is a requirement for tasks like grasping objects or navigating a room." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
7. **On Generalization Across Environments:** "A model trained on multiple robotic platforms can adapt more easily to a new, unseen robot." — [*Source: \[PaLM-E: An Embodied Multimodal Language Model*](https://arxiv.org/abs/2303.03378?ref=antoinebuteau.com)*\]*
8. **On Multimodal Prompts for Robots:** "Providing an image of a goal state alongside a text command gives the robot a clearer target than text alone." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
9. **On Real-World Constraints:** "Physics engines and real-world safety limits must guide the outputs generated by the language model." — [*Source: \[PaLM-E: An Embodied Multimodal Language Model*](https://arxiv.org/abs/2303.03378?ref=antoinebuteau.com)*\]*

### Part 5: AI in Healthcare and MedPaLM

1. **On Clinical Accuracy:** "Applying language models to medicine requires strict alignment to clinical facts, as hallucinated information has direct patient safety implications." — [*Source: \[Large Language Models Encode Clinical Knowledge*](https://arxiv.org/abs/2212.13138?ref=antoinebuteau.com)*\]*
2. **On Medical Question Answering:** "Evaluating a model against questions from medical licensing exams provides a baseline measure of its factual knowledge." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
3. **On Instruction Tuning for Health:** "Fine-tuning a general model on a curated dataset of medical question and answer pairs improves its ability to respond appropriately to clinical queries." — [*Source: \[Large Language Models Encode Clinical Knowledge*](https://arxiv.org/abs/2212.13138?ref=antoinebuteau.com)*\]*
4. **On Explainability:** "A medical model must provide the reasoning behind its answers, allowing clinicians to verify its logic." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
5. **On Human Evaluation:** "Automated metrics fall short for medical AI; outputs require review by panels of physicians to ensure safety and clinical utility." — [*Source: \[Large Language Models Encode Clinical Knowledge*](https://arxiv.org/abs/2212.13138?ref=antoinebuteau.com)*\]*
6. **On Domain-Specific Multimodality:** "Future medical models must natively process imaging scans alongside patient notes." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
7. **On Privacy and Security:** "Training models on medical data requires strict adherence to privacy protocols to protect patient information." — [*Source: \[Large Language Models Encode Clinical Knowledge*](https://arxiv.org/abs/2212.13138?ref=antoinebuteau.com)*\]*
8. **On Diagnostic Assistance:** "The goal of medical AI is to serve as an assistant that surfaces relevant information to doctors, rather than replacing clinical judgment." — [*Source: \[Stanford MLSys Seminar Series*](https://mlsys.stanford.edu/?ref=antoinebuteau.com)*\]*
9. **On Handling Uncertainty:** "When a medical query is ambiguous, the model must know when to ask clarifying questions or express uncertainty." — [*Source: \[Large Language Models Encode Clinical Knowledge*](https://arxiv.org/abs/2212.13138?ref=antoinebuteau.com)*\]*

### Part 6: Agentic AI and Autonomous Systems

1. **On the Shift to Agents:** "The industry is moving from models that answer static questions to agents that can plan and execute multi-step workflows." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
2. **On Error Recovery:** "A true agent must recognize when it has made a mistake and backtrack to find an alternative solution." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*
3. **On Tool Use:** "Agents need the ability to interact with external environments, like running code or searching the web, to verify their assumptions." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
4. **On Trajectory Data:** "Training agents requires datasets that map the entire step-by-step trajectory of solving a problem, instead of only the final answer." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*
5. **On Pre-training for Agency:** "We need to incorporate agentic behavior into the pre-training objective instead of relying solely on reinforcement learning post-training." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
6. **On Long-Horizon Planning:** "Maintaining context over a sequence of actions that spans hours or days remains a primary challenge for autonomous systems." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*
7. **On Self-Correction:** "Models need to generate their own feedback loops, evaluating the success of their actions and adjusting their strategies." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
8. **On API Interaction:** "Teaching models to read documentation and formulate correct API calls dynamically is necessary for extending their capabilities." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*
9. **On State Tracking:** "An agent must maintain an internal representation of the environment state to predict the outcomes of its future actions." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
10. **On Evaluation Paradigms:** "Measuring agent performance requires interactive environments where the agent decisions affect the state, unlike static datasets." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*

### Part 7: Reinforcement Learning for Coding

1. **On Code Generation:** "Writing code is inherently verifiable; an agent can run the code, observe the error, and iterate until the tests pass." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*
2. **On RL in Coding:** "Reinforcement learning is well-suited for coding tasks because the compiler provides an immediate, objective reward signal." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
3. **On Inference-Time Scaling:** "Allowing an agent to spend more compute time exploring different solutions during inference often yields better results than zero-shot generation." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*
4. **On Search Algorithms:** "Combining language models with tree-search algorithms enables the system to evaluate multiple coding paths simultaneously." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
5. **On Repository Context:** "To be useful in production, an autonomous coder must understand the entire repository structure rather than isolated files." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*
6. **On Debugging Capability:** "The ability to read stack traces and pinpoint the logical error in a script is distinct from the ability to write code from scratch." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
7. **On Synthetic Data Generation:** "Models can write their own coding challenges and solutions to generate synthetic training data, bootstrapping their capabilities." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*
8. **On Verifiable Rewards:** "In domains where the correctness of an answer can be programmatically verified, reinforcement learning scales effectively." — [*Source: \[TWIML AI Podcast*](https://twimlai.com/podcast/twimlai/episodes/rethinking-pre-training-for-agentic-ai/?ref=antoinebuteau.com)*\]*
9. **On the Role of the Developer:** "As coding agents become more autonomous, the developer role shifts towards defining high-level specifications and reviewing architecture." — [*Source: \[AI Engineer World's Fair*](https://youtube.com/watch?v=AI-Engineer-Worlds-Fair&ref=antoinebuteau.com)*\]*

### Part 8: Early Research: Networks and Wireless Systems

1. **On Video Surveillance Systems:** "Designing wireless video networks requires optimizing the transmission protocols to handle high bandwidth streams without dropping frames." — [*Source: \[The Design and Implementation of a Wireless Video Surveillance System*](https://dl.acm.org/doi/10.1145/2789168.2790100?ref=antoinebuteau.com)*\]*
2. **On Drone Hotspots:** "Mobile drones acting as aerial hotspots must dynamically adjust their user scheduling to account for changing channel conditions." — [*Source: \[Aerial Channel Prediction and User Scheduling in Mobile Drone Hotspots*](https://ieeexplore.ieee.org/document/8359287?ref=antoinebuteau.com)*\]*
3. **On Channel Prediction:** "Predicting the signal strength of a moving node allows the network to preemptively route traffic, reducing latency." — [*Source: \[Princeton PAWS Lab*](https://paws.princeton.edu/?ref=antoinebuteau.com)*\]*
4. **On Interdisciplinary Engineering:** "Solving problems in mobile networks often requires combining hardware design, signal processing, and distributed algorithms." — [*Source: \[Stanford Engineering Alumni Interview*](https://engineering.stanford.edu/?ref=antoinebuteau.com)*\]*
5. **On Spectrum Allocation:** "Efficient use of the wireless spectrum requires algorithms that can quickly reassign frequencies as users move between access points." — [*Source: \[Aerial Channel Prediction and User Scheduling in Mobile Drone Hotspots*](https://ieeexplore.ieee.org/document/8359287?ref=antoinebuteau.com)*\]*
6. **On System Reliability:** "A wireless system deployed in a physical environment must account for interference and physical obstacles in its routing logic." — [*Source: \[The Design and Implementation of a Wireless Video Surveillance System*](https://dl.acm.org/doi/10.1145/2789168.2790100?ref=antoinebuteau.com)*\]*
7. **On Edge Computing:** "Processing data closer to the source, such as on the drone itself, reduces the burden on the central network infrastructure." — [*Source: \[Princeton PAWS Lab*](https://paws.princeton.edu/?ref=antoinebuteau.com)*\]*
8. **On Mobility Management:** "Tracking user movement patterns helps the network anticipate handoffs between different mobile hotspots." — [*Source: \[Aerial Channel Prediction and User Scheduling in Mobile Drone Hotspots*](https://ieeexplore.ieee.org/document/8359287?ref=antoinebuteau.com)*\]*
9. **On Foundational Skills:** "The engineering rigor required to stabilize distributed wireless networks translates directly to the challenges of scaling large machine learning clusters." — [*Source: \[Stanford Engineering Alumni Interview*](https://engineering.stanford.edu/?ref=antoinebuteau.com)*\]*