Classical Reinforcement Learning and the Reward Signal

As a prelim to understanding RLHF, it pays to understand classical reinforcement learning, which has been studied under many different guises within computer science and more traditional engineering.

This is concerned with how an agent should take actions in a dynamic environment in order to maximise a reward signal.

Back to LLM Basics - What is RLHF

RLHF is a core part of how large language models learn. 

It stands for reinforcement learning with human feedback.  Idea is you train models to align with human preferences.

A core input is data from human annotators as an input into the learning process.

The algorithm now widely used in RLHF was pioneered by OpenAI (InstructGPT). The idea is you align the model with human intent. Alignment with human intent is what makes the model (more) useful.

Document Intelligence Infrastructure

Article covers GraphRAG and an agentic approach as a path to document intelligence,

GPT-5.6 Sol - Preview

Sol is OpenAI's strongest model yet.

It has a robust safety stack, including protections for higher-risk activity and repeated misuse.

A limited preview is being done with trusted partners first.

Codex is now the ChatGPT App

Inside the app it says "Powered by Codex and OWL". Codex is the coding agent, OWL is the Chromium-based environment that provides the "runtime shell" for Codex.

OpenCV in Python

pip install opencv-python

More on OpenCV on opencv.org.

Reverse Debugging

Not all debuggers are created equal. Some allow reverse debugging, some do not.

gdb does

pdb does not

pdb is a forward only debugger. Once a statement runs, its side-effects (e.g. variable changes, I/O etc.) are permanent unless you restart.