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AI & ML/ Writing/Tagged “information-theory”

Tagged “information-theory”

4 posts.

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All Model Architecture19 Training & Alignment22 Inference & Serving20 Agents & Orchestration11 Reasoning & Evaluation27 Safety, Security & Governance7 Platforms & Practice22
Inference & Serving 27 min

Everything Is Lossy Compression: A Rate-Distortion View of Quantisation, KV Caches, and Distillation

Weight quantisation, KV cache eviction, prompt compression and distillation are treated as four separate engineering disciplines with four separate literatures. They are one problem: choosing a point on a rate-distortion curve. S…

information-theory quantisation kv-cache distillation ∑ ◫
Training & Alignment 22 min

Language Modelling Is Compression: The Seventy-Year-Old Idea Underneath Every LLM

In 1951 Claude Shannon estimated the entropy of English by having people guess the next letter. In 2023 a 70-billion-parameter language model compressed a gigabyte of Wikipedia to 8.3% of its size, beating every compressor ever p…

information-theory compression entropy language-models ∑ ◫
Platforms & Practice 26 min

One Number, Two Knobs: The ELBO Is a Rate-Distortion Tradeoff

Two variational autoencoders can report the same ELBO to three decimal places while one has a latent code carrying five bits of information about the input and the other has a latent carrying exactly zero. The objective cannot se…

vae elbo rate-distortion representation-learning ∑ ◫
Training & Alignment 25 min

The Four Faces of KL Divergence: Mode-Seeking, Mode-Covering, and Why Your Estimator Went Negative

You add a KL penalty to an RLHF objective, log it, and it prints minus 0.03. KL divergence is provably non-negative, and nothing is broken. One formula does four different jobs in modern machine learning, and almost every confusi…

math information-theory kl-divergence rlhf ∑ ◫
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