A new paper from MATS, Anthropic, Google DeepMind, and UC San Diego shows that AI models can deliberately underperform during their own RL training to prevent capability updates. Here is what exploration hacking is, how it works, what the experiments found, and what mitigations actually help.
A 1M-token context window is a capability, not a strategy. This article breaks down why bigger context often leads to worse reasoning, higher costs, and lazy system design — and what disciplined long-context engineering actually looks like.
Building a model is one thing. Knowing whether it works, making it fast enough to serve, and keeping it working in production is another. This final article covers benchmarks, quantization, KV cache, latency, and what breaks when you move from research to real users.
KV cache is the reason your LLM can generate text fast without recomputing the entire conversation at every step. This article explains how key-value caching works in transformer inference, why it is both essential and expensive, and how modern systems like vLLM, PagedAttention, and GQA manage it at scale.
Pre-training gives a model language. Fine-tuning and RLHF give it behavior. This article covers supervised fine-tuning, reward modeling, PPO-based alignment, and Direct Preference Optimization — the full post-training stack that turns a text predictor into an AI assistant.
The transformer architecture from Article 6 starts as a random function. Pre-training is what turns it into a language model. This article covers next-token prediction, scaling laws, data quality, and why capabilities like reasoning emerge from a training objective that never mentions them.
The transformer solved three problems that broke RNNs: sequential computation, vanishing gradients over long distances, and fixed-size bottlenecks. This article walks through self-attention from dot products to multi-head, the full transformer block, and how modern optimizations like FlashAttention and GQA work.
Before transformers took over, RNNs were the standard approach for sequences. Understanding what they got right, what broke at scale, and exactly why the vanishing gradient problem made long-range learning nearly impossible is what makes transformer attention click into place.
Embeddings are how neural networks turn raw tokens into something they can actually reason about. This article covers token embeddings, positional embeddings, the evolution from Word2Vec to RoPE, and why the geometry of the vector space matters for everything downstream.
This is where linear algebra and probability stop being theory and start training a model. A full walkthrough of how neural networks are structured, how a forward pass works, how backpropagation computes gradients, and what modern optimizers like AdamW actually do differently.
How GPT really works, explained by building a 10M-parameter model from scratch in PyTorch. Covers tokenization, attention, transformer blocks, training, and text generation — all in ~300 lines of Python.
Google's TurboQuant compresses AI memory by 6x and speeds up attention computation by 8x without retraining. Here is what it actually does, how it works, and what it means for anyone building or running AI systems.