I contextualize and explain important topics in AI research.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
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| Issues | 110 | Founded | 4 years ago | Last Issue | 9 days ago |
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cameronrwolfe.substack.comLatest Issues
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...
RL Scaling Laws for LLMs
(from [1, 2, 3])
Scaling is one of the most impactful concepts in the history of AI research. For large language models (LLMs), scaling has mostly been studied in the context of pretraining, where rigorous scaling laws have allowed us to c...
The Anatomy of an LLM Benchmark
(from [2, 3, 4, 10, 12])
Throughout the history of AI research, progress has been measured—and accelerated—by high-quality benchmarks. AI is an empirical field that is driven by discovering interventions that improve performance on key ben...
Applying Statistics to LLM Evaluations
(from [1, 2, 3])
Research on large language models (LLMs) is empirically driven. For this reason, model evaluations play a pivotal role in the field’s progress. We improve models by making changes, evaluating them, and iterating. Despite t...
Rubric-Based Rewards for RL
(from [1, 2, 3, 5, 16])
Many of the recent capability gains in large language models (LLMs) have been a product of advancements in reinforcement learning (RL). In particular, RL with verifiable rewards (RLVR) has drastically improved LLM c...
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Research @ Netflix • Rice University PhD • I make AI understandable
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