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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 görüntüleme • 6 ay önce •via X (Twitter)

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David McAllister profil fotoğrafı

David McAllister6 ay önce

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

Kush Hari profil fotoğrafı

Kush Hari6 ay önce

You just got… Mcallister’d!!!

Samarth Sinha profil fotoğrafı

Samarth Sinha6 ay önce

My hero 😍😍😍😍😍

atharva ☆ profil fotoğrafı

atharva ☆6 ay önce

wow

Siddharth Ancha profil fotoğrafı

Siddharth Ancha6 ay önce

Really cool work @davidrmcall!!!

James Thewlis profil fotoğrafı

James Thewlis6 ay önce

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

Ritvik Singh profil fotoğrafı

Ritvik Singh6 ay önce

Congrats, David!

Phúc profil fotoğrafı

Phúc6 ay önce

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

Ronald Skorobogat profil fotoğrafı

Ronald Skorobogat6 ay önce

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?

David McAllister profil fotoğrafı

David McAllister6 ay önce

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?

AqibAi profil fotoğrafı

AqibAi6 ay önce

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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