Tomer Galanti has written a clear explainer of Distribution-Aware Programming, the blog post accompanying the paper Distribution-Aware Algorithm Design with LLM Agents.
The post explains the central idea behind distribution-aware program learning: when the learned object is executable code, generalization is not only about correctness, but also about runtime on the deployment distribution. Samples from a recurring workload can reveal reusable structure, which an LLM agent can turn into specialized solver code.
Read the full post on the DL Fundamentals Lab blog.