I contextualize and explain important topics in AI research.
| Platform |  | Pricing | Freemium | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 114 | Founded | 4 years ago | Last Issue | 14 days ago |
| Active | |||||
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cameronrwolfe.substack.comLatest Issues
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...
Agentic RL: Frameworks and Best Practices
(from [3, 4, 6])
Recent research on large language models (LLMs) has been heavily focused on reasoning and reinforcement learning (RL). Much of the initial work in this area considered static tasks, where the LLM generates a single respons...
Agent Evaluation: A Detailed Guide
(from [1, 3, 8, 12])
Evaluation is one of the most important research areas for large language models (LLMs). Recently, patterns in LLM usage and evaluation have drastically changed. Whereas we previously evaluated LLMs using benchmarks co...
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Research @ Netflix • Rice University PhD • I make AI understandable
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