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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 次观看 • 6 个月前 •via X (Twitter)

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David McAllister 的头像

David McAllister6 个月前

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 的头像

Kush Hari6 个月前

You just got… Mcallister’d!!!

Samarth Sinha 的头像

Samarth Sinha6 个月前

My hero 😍😍😍😍😍

atharva ☆ 的头像

atharva ☆6 个月前

wow

Siddharth Ancha 的头像

Siddharth Ancha6 个月前

Really cool work @davidrmcall!!!

James Thewlis 的头像

James Thewlis6 个月前

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

Ritvik Singh 的头像

Ritvik Singh6 个月前

Congrats, David!

Phúc 的头像

Phúc6 个月前

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 的头像

Ronald Skorobogat6 个月前

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 的头像

David McAllister6 个月前

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 的头像

AqibAi6 个月前

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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