Research desk
The papers behind how we build
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.
All entries
2 entries
- 2022PreprintCurated
Constitutional AI: Harmlessness from AI Feedback
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.
- 2022NeurIPS 2022PublishedCurated
Training Language Models to Follow Instructions with Human Feedback
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.
Browse by topic
Foundations
The architectures and pre-training results everything else stands on.
3 entries
Retrieval & RAG
Grounding a model in documents you actually own.
5 entries
Reasoning
Getting a model to show its work, and checking the work.
6 entries
Agents & Tool Use
Models that call tools, hold state, and finish multi-step jobs.
8 entries
Adaptation
Changing what a model does without retraining all of it.
4 entries
Alignment
Steering outputs toward what a person would accept.
6 entries
Evaluation
How you know it works before a customer finds out it does not.
11 entries