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Math-to-Manim

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Re: Math-to-Manim

#2

Post by madanparas »

The "RL repair loop" is iterative LLM prompting with stderr feedback, not reinforcement learning. There is no training code, no reward function, and no environment in the repo. The loop also freezes the scene spec and only regenerates code, so if the planner specified 12 objects that geometrically do not fit on screen, three repair attempts will not help.

Re: Math-to-Manim

#3

Post by tptacek »

madanparas wrote: post #2

The "RL repair loop" is iterative LLM prompting with stderr feedback, not reinforcement learning. There is no training code, no reward function, and no environment in the repo. The loop also freezes the scene spec and only regenerates code, so if the planner specified 12 objects that geometrically do not fit on screen, three repair attempts will not help.

Thanks, I was wondering what this README could have meant by "RL loop" here.

Re: Math-to-Manim

#4

Post by geuis »

Entire article reads as output from a well structured prompt. It's almost point for point style-wise when I ask for a summary for current repo changes before deciding to do the commit.

Re: Math-to-Manim

#6

Post by sheept »

iosovi wrote: post #5

Did anybody else notice the random "Christian" in the README?

probably intended to be a signature for the preface, since Christian is the repo owner's name, that turned into a list because of markdown syntax

Re: Math-to-Manim

#7

Post by yorwba »

madanparas wrote: post #2

The "RL repair loop" is iterative LLM prompting with stderr feedback, not reinforcement learning. There is no training code, no reward function, and no environment in the repo. The loop also freezes the scene spec and only regenerates code, so if the planner specified 12 objects that geometrically do not fit on screen, three repair attempts will not help.

There's no training code because the author is using an external service for that https://docs.primeintellect.ai/hosted-training/getting-start... The reward function is https://github.com/HarleyCoops/Math-To-Manim/blob/d1c412d22a... The environment is iterative LLM prompting.

The idea is apparently that a model that is bad at fixing its own mistakes might become better if you train it on this task using reinforcement learning.

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