entropix

Seems similar to entropix? https://github.com/xjdr-alt/entropix

GPT-5 Pro is different from GPT-5 just like o3 Pro is different from o3 and o1 Pro is different from o1 :) OpenAI doesn't want to tell the truth, but it might be something similar in the spirit to e.g. Entropix. [0] [0] https://github.com/xjdr-alt/entropix

funny I was just looking at this approach and see what's new in the wild (a work in progress) see Entropix [1] by [2] [1] https://github.com/xjdr-alt/entropix [2] https://x.com/_xjdr

This group of conversations is what I was thinking of: https://x.com/seb_far/status/1803446067343556872 https://oatml.cs.ox.ac.uk/blog/2024/06/19/detecting_hallucin... Some people in the Open Source community I think implemented it a few months ago as Shrek Entropy Sampeling and it got a pop in circulation (https://github.com/xjdr-alt/entropix) best of luck!

Wow, a whole article that didn't mention the word "sampler" once. There's pretty strong evidence coming out that truncation samplers like min_p and entropix are strictly superior to previous samplers (which everyone uses like top_p) to prevent hallucinations and that LLMs usually "know" when they are "hallucinating" based on their logprobs. https://openreview.net/forum?id=FBkpCyujtS (min_p sampling, note extremely high review scores) https://github.com/xjdr-alt/entropix (Entropix) https://artefact2.github.io/llm-sampling/index.xhtml

Optillm (https://github.com/codelion/optillm) now has an implementation of entropy decoding based adaptive sampling based on the work of @\_xjdr (https://github.com/xjdr-alt/entropix). The original repo is in a state of flux but the idea seems to work well. I also did an eval of the entropy decoding on GSM8k with Qwen2.5-0.5B-Instruct model in a zero shot setting. I found improvements over the base model but they are not better than what we get with a much simple CoT decoding. You can try them both in this free Gogole Colab -(https://colab.research.google.com/drive/1SpuUb8d9xAoTh32M-9wJsB50AOH54EaH?usp=sharing)