Topic
Sampling And Temperature
5 episodes
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Overview: Decoding Strategy
We finally slow down on decoding strategy, the rule that turns a model's next-token odds into the actual words you see. We use one hallway-and-doors picture to make greedy decoding, sampling, top-k, top-p, beam search, and newer decoding work feel less like magic knobs.
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Overview: Sampling and Temperature
We’re finally slowing down and explaining sampling and temperature from the ground up, because we keep circling back to it and it’s one of those knobs that sounds simple until it isn’t. We talk through why the same prompt can produce different answers, how temperature changes the shape of the next-token choices, and where the trade-offs actually land.
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Overview: Calibration
We finally slow down and make calibration click: what it means for a model’s confidence to match reality, how you measure that, and why it matters when you actually want to trust the thing. We keep it grounded in the systems we’ve been circling for ages, because calibration is everywhere once you start looking.
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Concurrent Image Understanding and Generation: Self Correcting Coupled Markov Jump Processes
Fern and Lintel dig into a new paper on doing image understanding and image generation at the same time, inside one decoding loop. The hook is simple: most systems either describe first and draw later, or they run both sides in parallel without letting the latest text and image decisions correct each other mid-step. This paper tries to fix that with a coupled masked-diffusion sampler that can both coordinate and backtrack.
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Overview: Autoregressive Generation
We finally slow down and make autoregressive generation click: the whole thing is just a model writing one token, then using what it wrote to choose the next one. We keep the focus on the loop, the trade-offs, and why that one-step-at-a-time setup is still the backbone of modern language models.