Research desk
An annotated reading list. Every entry is credited to its original authors, with a note on what it changes once the idea meets a real system.
3 entries
Reformulates preference learning so the reward model can be folded into the policy in closed form. The result is a single supervised objective over preference pairs, with no separate reward model and no reinforcement learning loop.
Trains a model to critique and revise its own outputs against a written set of principles, then learns from the revised outputs. The aim is a helpful assistant that can explain why it declined, rather than one that simply refuses.
Fine-tunes language models on human demonstrations and then on human preference comparisons collected through reinforcement learning. The paper is the origin of the RLHF pipeline that produced the first widely used instruction-following assistants.
The architectures and pre-training results everything else stands on.
3 entries
Grounding a model in documents you actually own.
4 entries
Getting a model to show its work, and checking the work.
5 entries
Models that call tools, hold state, and finish multi-step jobs.
6 entries
Changing what a model does without retraining all of it.
3 entries
Steering outputs toward what a person would accept.
3 entries
How you know it works before a customer finds out it does not.
5 entries