TechnicalAI glossary
Reinforcement learning
A way of learning without examples to copy: the system tries, receives a reward signal when it succeeds (or a penalty when it does not), and gradually adjusts its strategy. This is how AlphaGo beat the Go champions, and it is a key step in shaping the behaviour of today's assistants, notably through RLHF.
In other words
Learning to ride a bike: nobody hands you the formula, you fall, you try again, and your body remembers what works.
Alyriaz articles that mention it
- This insect-sized robot does ten backflips in eleven seconds
A new AI control system allows a flying micro-robot from MIT to fly with the agility of an insect. We explain what this changes and what it could be used for.
- Mid-training, final fine-tuning: what happens after pre-training
Between a base model and a usable model, there are several distinct training phases. We explain what each one does and why that is where the value now lies.
- A startup is valued at 1.4 billion and doesn't train any model from scratch
A young Chinese company reaches a $1.42 billion valuation in seven months by specialising open models rather than training them. We analyse this model and what it reveals.
- GPT-6 Astra: the first model rated "critical" in cybersecurity, and two versions to handle it
On 3 September, OpenAI unveiled GPT-6 Astra, the first model to reach the critical cyber capability level under its own evaluation framework. We analyse the release, the two-tier structure, the AGI claim, and a finding on reasoning monitoring.
- GLM-5.3: an open model found over a thousand real flaws in widely used software
Z.ai's GLM-5.3 achieves top scores in vulnerability discovery. Unlike previous versions, its weights have not been released: the lab has withheld them for a security assessment. We analyse this precedent.
- What is reinforcement learning? Learning through trial, error, and rewards
Reinforcement learning involves learning through interaction and reward rather than by imitating examples. We explain the principle, its spectacular successes, and why it lies at the heart of current AI behaviour.