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In the previous chapter, we formalized the agent-environment interaction as a Markov decision process (MDP).
We began with the Markov property, which states that the future depends on the past only through the present state. This is the assumption that makes the entire framework tractable: once you know the current state, you can discard the history.
We then...
In the previous chapter, we introduced reinforcement learning as a third kind of machine learning, distinct from supervised and unsupervised learning.
We saw the core agent-environment loop, where the agent picks actions, the environment returns rewards and a new state, and the cycle continues.
We discussed four properties that make RL distinctive: feedback ...
On 5 March 2025, the Association for Computing Machinery announced that Andrew G. Barto and Richard S. Sutton had won the 2024 ACM A.M. Turing Award, the computing world's equivalent of the Nobel Prize.
The citation was specific: "for developing the conceptual and algorithmic foundations of reinforcement learning." Their 1998 textbook, "Reinforcement Learni...
In the previous article, we built a complete understanding of how diffusion language models work from first principles.
We started with the two structural bottlenecks in autoregressive (AR) generation.
First, sequential decoding is memory-bandwidth bound. The GPU spends the vast majority of its time shuttling weights from memory to compute cores, achi...