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When you prompt an LLM for code, you get one deterministic program. However, the LLM actually defines a distribution over many programs, and existing methods discard it‼️ PPoT uses this distribution to extract free performance and efficiency gains. 🧵👇

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Daniel Israel

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12,002 görüntüleme • 4 ay önce •via X (Twitter)

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What is probabilistic programming? 🤔 It is just like regular coding, but instead of constants, it uses random variables with probability distributions. As a result, a single probabilistic program encodes a distribution over exponentially many deterministic programs.

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Normally, code generation frameworks prompt an LLM, test the output, and if the code fails, just resample from the LLM. PPoT can obtain multiple programs from just a single LLM sample by converting key tokens into random variables parameterized by the LLM's logits.

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PPoT leads to significant accuracy gains in a Best-of-N setting. Extracting additional PPoT samples per autoregressive sequence drastically improves the accuracy curve with zero GPU overhead.

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PPoT fundamentally alters inference scaling laws. Modeling the error rate with a power law reveals that PPoT exhibits a strictly better exponent in the number of LLM samples. The error rate drops faster, meaning the computational surplus from PPoT actually accelerates with additional test-time compute.

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It was a pleasure to collaborate with @PoorvaGarg11, @renatogeh, Todd Millstein, Kyle Richardson, and @guyvdb 📰 Paper : 🖥️ Code:

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My friend applied to 200 tech jobs in two years. No PhD. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me a course that was never supposed to get out. A 3-hour video to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 05:43 - LLM transformer architecture • 40:24 - training the LLM • 1:30:27 - modernizing the LLM • 2:33:53 - scaling the LLM

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