#post-training

Tagged post-training · show all articles

A robotic picker arm in a checkpoint tape vault caught mid-transfer, one cartridge lifted clear of its slot and not yet seated in the drive's read head, rows of cartridge cases receding beyond it

Claude, From First Principles: Training, Constitutional Methods, and What Actually Shapes a Response

A deployed Claude model is a pretrained prior, a post-training pass partly shaped by written principles, a handful of per-request settings, and a documented evaluation gate — four separable things, not one object, and each changes the answer differently.

By Brecht Corbeel · August 11, 2026 · 15 min read

A row of rater workstations in a bright annotation lab, the nearest screen showing one prompt above two candidate responses in side-by-side panes with neither of them chosen yet, an annotation guideline binder open on the desk and racks of training nodes standing behind

Alignment & Safety

What RLHF Actually Optimises: Rated Agreeableness, and Where It Parts from Helpfulness

Reinforcement learning from human feedback does not install correctness. It fits a scalar model of what raters approved of and then pushes a policy up that surface — which is a different thing, in identifiable places.

By Brecht Corbeel · August 8, 2026 · 17 min read

A checkpoint storage array of drive caddies with one caddy caught part-way into its bay on its rails, latch still open, and rows of GPU pods receding beyond it under an overhead busway

Foundation Models

OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute

A frontier model is a budget divided three ways. Where the compute goes—into weights, into behaviour, or into each individual answer—determines almost everything else about the system.

By Brecht Corbeel · August 8, 2026 · 12 min read

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