I contextualize and explain important topics in AI research.
| Platform |  | Pricing | Freemium | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 116 | Founded | 4 years ago | Last Issue | 17 days ago |
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Evaluating Agents on Highly Subjective Tasks
This overview was written as a corresponding reference for my presentation at the AI Engineer (AIE) conference. You can find the slides for the talk here. A full video of the talk is available here.
Evaluation is one of the most dif...
Notes on NVIDIA Nemotron
A broad coverage of the most practically useful series of open LLM tech reports...
Throughout the history of AI, open research has played a critical role in driving progress. Today, many key details of frontier large language models (LLMs)...
Reinforcement Learning for LLMs: The Complete Guide
(from [8, 12, 14])
Reinforcement learning (RL) has played a crucial role throughout the history of research on large language models (LLMs). Through RL, we created early versions of instruction following models, made important advancements...
Notes on Midtraining
Creating better specialized LLMs with midtraining and continual pretraining...
The training process for large language models (LLMs) has historically been composed of two high-level stages: pretraining and post-training. Pretraining expose...
Agentic World Models
(from [1, 3, 4])
Reinforcement learning (RL) has played a key role in the expansion of capabilities for large language model (LLM) agents. Despite the many benefits of RL, however, this training strategy has a key limitation—the learning s...
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Authors
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Research @ Netflix • Rice University PhD • I make AI understandable
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