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Eric
1,092 posts
user avatar
Eric
@ericmitchellai
chatgpt posttraining @openai. building personal agi. I like ai and music and some other stuff
United States
ericmitchell.ai
Joined December 2017
592
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12.3K
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  • user avatar
    Eric
    @ericmitchellai
    Aug 11, 2025
    > GPT-5 is the first series of models that actually doesn’t hallucinate basically at all *real-world utility-maxxing instead of benchmark-maxxing intensifies* Disclaimer: GPT-5 is still not perfect and may make (far fewer now) mistakes
    user avatar
    Max Weinbach
    Creative Strategies, Inc
    @mweinbach
    Aug 11, 2025
    GPT-5 is the first series of models that actually doesn’t hallucinate basically at all, especially when given mildly business logic/models/research notes and having it work with the data
    1M
  • user avatar
    Eric
    @ericmitchellai
    Jan 27, 2023
    ChatGPT (and others) generate very fluent (but not always truthful) text. Some worry that teachers, news-readers (like you!), and society in general will be swamped with AI-generated content. That's why we built DetectGPT, a method for detecting if text comes from an LM.
    235K
  • user avatar
    Eric
    @ericmitchellai
    May 30, 2023
    RLHF is the 🪄 getting us from GPT-3 to ChatGPT. But RLHF is hard! Need to train a reward model, then do RL on a big LM (w/ expensive sampling & tuning) 𝙊𝙧 𝙙𝙤 𝙮𝙤𝙪? Introducing Direct Preference Optimization (DPO), a simple classification loss provably equivalent to RLHF
    223K
  • user avatar
    Eric
    @ericmitchellai
    Aug 23, 2025
    Seems like people are really coming around to codex cli w/ gpt5-high!! What is codex cli still missing? How can we make it even better??
    user avatar
    Taelin
    @VictorTaelin
    Aug 21, 2025
    BTW, I've basically stopped using Opus entirely and I now have several Codex tabs with GPT-5-high working on different tasks across the 3 codebases (HVM, Bend, Kolmo). Progress has never been so intense. My job now is basically passing well-specified tasks to Codex, and reviewing
    183K
  • user avatar
    Eric
    @ericmitchellai
    Oct 27, 2023
    RLHF is powerful; it lets us fine-tune LLMs to be more useful. What if we could do RLHF… without fine-tuning??? Excited to share Emulated Fine-Tuning (EFT)! EFT lets us “emulate” what we would have gotten if we did RLHF on a new model, without actually doing the RLHF!
    73K
  • user avatar
    Eric
    @ericmitchellai
    Jun 22, 2023
    DPO (fast, simple, performant RLHF) code is here! With DPO there's 𝗻𝗼 𝗿𝗲𝘄𝗮𝗿𝗱 𝗺𝗼𝗱𝗲𝗹 𝗼𝗿 𝗥𝗟 𝗻𝗲𝗲𝗱𝗲𝗱. It's finally easy to fine-tune llama from human preferences 😊 github.com/eric-mitchell/… Can't wait to see the cool models people train with it 🤓
    GitHub - eric-mitchell/direct-preference-optimization: Reference implementation for DPO (Direct...
    From github.com
    92K
  • user avatar
    Eric
    @ericmitchellai
    Jan 24, 2024
    After 5 years of PyTorch, I have had enough of doing ML on easy mode. Starting today, I am switching exclusively to TF for a real challenge 🫡🫡
    83K
  • user avatar
    Eric
    @ericmitchellai
    Nov 5, 2025
    Powerful models are... powerful, yes. But making them more collaborative and steerable is equally important in making them useful! This update is a huge usability win and I'm curious to hear what people think of it!
    user avatar
    OpenAI
    @OpenAI
    Nov 5, 2025
    You can now interrupt long-running queries and add new context without restarting or losing progress. This is especially useful for refining deep research or GPT-5 Pro queries as the model will adjust its response with your new requirements. Just hit update in the sidebar and
    00:00
    50K
  • user avatar
    Eric
    @ericmitchellai
    Aug 10, 2025
    Important GPT-5 PSA; if you want an answer that is maximally correct, do tell the model to think hard in your prompt. It literally will do so clearly we failed to communicate this well, apologies for that
    user avatar
    Jeremy Howard
    @jeremyphoward
    Aug 9, 2025
    PSA: Add ". think hard" to the end of all your ChatGPT GPT5 prompts. In my testing, so far that has resulted in it using the competent model 100% of the time. And so far, not adding it has resulted in it using the crippled model 100% of the time, which has failed all my tasks.
    74K
  • user avatar
    Eric
    @ericmitchellai
    Nov 24, 2023
    advisors say I should be “not doing research” & “getting a job” alas due to recent RLHF DPO/IPO/PPO debates I wrote a 1pg mini-paper ericmitchell.ai/cdpo.pdf tldr: assuming noisy pref data gives a 'conservative DPO', might make DPO stabler late in training (& looks like IPO) 🧵
    95K
  • user avatar
    Eric
    @ericmitchellai
    Jan 11, 2024
    Replying to @AndrewYNg @rm_rafailov and 4 others
    Thank you Andrew- it means a lot! We took this photo together 6.5 years ago, in the summer of 2017, when I was just getting started in research... Thank you for the insight and inspiration 🫡
    27K
  • user avatar
    Eric
    @ericmitchellai
    Aug 10, 2025
    this is what progress looks like
    user avatar
    Chubby♨️
    @kimmonismus
    Aug 10, 2025
    GPT-5 admits it "doesn't know" an answer! This is one of the huge improvements over previous models: instead of hallucinating, it lets you know its limits.
    14K
  • user avatar
    Eric
    @ericmitchellai
    Nov 1, 2023
    ChatGPT users know the dreaded “as of my knowledge cutoff…” Can we keep LLMs up-to-date with continual fine-tuning? Our EMNLP paper shows LMs may remember only a *tiny* fraction of the info they see in a data stream It also shows meta-learning can improve knowledge uptake 🥹
    93K
  • user avatar
    Eric
    @ericmitchellai
    Jul 28, 2023
    Curious how to take the RL out of RLHF? Come check out our #ICML2023 workshop poster for Direct Preference Optimization (aka, how to optimize the RLHF objective with a simple classification loss)! Meeting Room 316 AB, 10am/12:20/2:45 Hawaii time sites.google.com/view/mfpl-icml…
    user avatar
    Eric
    @ericmitchellai
    May 30, 2023
    RLHF is the 🪄 getting us from GPT-3 to ChatGPT. But RLHF is hard! Need to train a reward model, then do RL on a big LM (w/ expensive sampling & tuning) 𝙊𝙧 𝙙𝙤 𝙮𝙤𝙪? Introducing Direct Preference Optimization (DPO), a simple classification loss provably equivalent to RLHF
    56K

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