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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 Aufrufe • vor 6 Monaten •via X (Twitter)

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11 Kommentare

Profilbild von David McAllister

David McAllistervor 6 Monaten

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

Profilbild von Kush Hari

Kush Harivor 6 Monaten

You just got… Mcallister’d!!!

Profilbild von Samarth Sinha

Samarth Sinhavor 6 Monaten

My hero 😍😍😍😍😍

Profilbild von atharva ☆

atharva ☆vor 6 Monaten

wow

Profilbild von Siddharth Ancha

Siddharth Anchavor 6 Monaten

Really cool work @davidrmcall!!!

Profilbild von James Thewlis

James Thewlisvor 6 Monaten

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

Profilbild von Ritvik Singh

Ritvik Singhvor 6 Monaten

Congrats, David!

Profilbild von Phúc

Phúcvor 6 Monaten

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

Profilbild von Ronald Skorobogat

Ronald Skorobogatvor 6 Monaten

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?

Profilbild von David McAllister

David McAllistervor 6 Monaten

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?

Profilbild von AqibAi

AqibAivor 6 Monaten

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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