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We developed a simple, sample-efficient online RL technique for post-training image generation models. We see it as a possible steerable alternative to CFG, driven by any scalar reward, including human preference.

David McAllister

1,120 subscribers

70,991 просмотров • 6 месяцев назад •via X (Twitter)

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Комментарии: 11

Фото профиля David McAllister

David McAllister6 месяцев назад

Check out our blog post at for a walkthrough of our design decisions. w/ fantastic collaborators: Miika Aittala, Tero Karras, Janne Hellsten, @akanazawa Timo Aila and Samuli Laine

Фото профиля Kush Hari

Kush Hari6 месяцев назад

You just got… Mcallister’d!!!

Фото профиля Samarth Sinha

Samarth Sinha6 месяцев назад

My hero 😍😍😍😍😍

Фото профиля atharva ☆

atharva ☆6 месяцев назад

wow

Фото профиля Siddharth Ancha

Siddharth Ancha6 месяцев назад

Really cool work @davidrmcall!!!

Фото профиля James Thewlis

James Thewlis6 месяцев назад

Epoch 0 is not a happy kitty 😂 very cool work!

Фото профиля Ritvik Singh

Ritvik Singh6 месяцев назад

Congrats, David!

Фото профиля Phúc

Phúc6 месяцев назад

Hi McAllister. Thanks for your interesting work. I found that the idea of “copying gradient at x0” to any intermediate state x_t is quite similar to DRTune techniques - also a method to finetune text-to-images model with reward

Фото профиля Ronald Skorobogat

Ronald Skorobogat6 месяцев назад

really cool work. the uniform copy beating backprop-through-chain (rows I vs J) leans on flow jacobians being roughly psd via ot straightness. did you try it on distilled few-step students or late-training checkpoints where flows get more rotational?

Фото профиля David McAllister

David McAllister6 месяцев назад

Thanks! We didn’t try with distilled students though that sounds promising. Our experiments initialized from pretrained SD3 checkpoints, would you consider that late training?

Фото профиля AqibAi

AqibAi6 месяцев назад

Interesting direction—especially the “any scalar reward” framing makes it feel much more general than CFG. Curious how stable it is in practice across different reward signals, and how you’re handling reward hacking / drift during online updates.

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