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
- 2023NeurIPS 2023PublishedCurated
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
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.
- 2023NeurIPS 2023PublishedCurated
QLoRA: Efficient Finetuning of Quantized LLMs
Backpropagates through a frozen 4-bit quantized base model into a small set of low-rank adapters. The approach brings fine-tuning of large models down to a single consumer GPU while holding quality close to full 16-bit tuning.
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