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Introducing Flow Reasoning Models. We developed a recurrent flow-based architecture to efficiently solve structured reasoning problems (e.g., Sudoku). FRMs apply continuous flows to discrete data and recurrently refine their past mistakes through self-conditioning.

Alec Helbling

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129,816 次观看 • 1 个月前 •via X (Twitter)

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46 条评论

Alec Helbling 的头像

Alec Helbling1 个月前

FRMs are dramatically more efficient at inference than competing methods like HRM, TRM, and masked diffusion. On Sudoku-Extreme, FRMs match the next best method EqR’s 98.7% solve rate with 44× fewer FLOPs and reach a higher 99.5% peak accuracy.

Alec Helbling 的头像

Alec Helbling1 个月前

Unlike conventional self-conditioning training, we apply multiple recurrent passes to the self-conditioned flow. This mitigates exposure bias in the self-conditioned carry state and dramatically improves performance (e.g., Sudoku-Extreme goes from ~30% to 99%).

Alec Helbling 的头像

Alec Helbling1 个月前

Flow Reasoning Models admit a surprisingly elegant dynamical-systems perspective. Correct answers are fixed-point attractors of the self-conditioned flow’s dynamics.

Alec Helbling 的头像

Alec Helbling1 个月前

Link to paper here:

Alec Helbling 的头像

Alec Helbling1 个月前

Code coming soon:

KyberNuII 的头像

KyberNuII1 个月前

So basically with Flow Matching

λux 的头像

λux1 个月前

@nopainkiller waow this is really cool research direction!

Alec Helbling 的头像

Alec Helbling1 个月前

@nopainkiller Ty ty!

AI Quanting 的头像

AI Quanting1 个月前

honestly the 30 to 99 ablation is more interesting than the 44x. training on the messy states the model actually hits at inference instead of clean ones is the scheduled sampling idea again, and it looks like thats doing more of the work than the flow part

Stefano Martiniani 的头像

Stefano Martiniani1 个月前

Cool, seems related to

Alec Helbling 的头像

Alec Helbling1 个月前

Nice! I'll check it out.

Stefano Martiniani 的头像

Stefano Martiniani1 个月前

Someone on your team came across it, we just realized it was cited in v1, alongside Biroli and Mezard's work which is also related (you can find more relevant citations in our paper). Wyart also looked at cycling diffusion around the same time as us arXiv:2605.27006

Manuel Brack 的头像

Manuel Brack1 个月前

Alec really does have the most pleasing visuals for his work.

Antonio Valerio Miceli Barone 的头像

Antonio Valerio Miceli Barone1 个月前

Ouch, scooped. Good work.

Raymond Weitekamp 的头像

Raymond Weitekamp1 个月前

looks very interesting! can't wait to see the code!

Gregor 的头像

Gregor1 个月前

When you say self-conditioning, is it conditioning on previous logits or on a decoded discrete sample at each recurrent step?

Alec Helbling 的头像

Alec Helbling1 个月前

Good question. We condition on the previous logits rather than the decoded sample. These logits retain valuable information.

YingTangLab 的头像

YingTangLab1 个月前

Great work! I wonder how FRM compares to ERM, which also reaches peak accuracy, given that their names happen to be very close😃

parthak ✧ 的头像

parthak ✧1 个月前

Woww amazing

Zhihan Yang 的头像

Zhihan Yang1 个月前

What is the "Adaptive MDLM" used in this work? Is this the top-probability-margin sampler from Kim et al?

Alec Helbling 的头像

Alec Helbling1 个月前

Yep!

Sonnen 的头像

Sonnen1 个月前

Nice

bill 的头像

bill1 个月前

Coll

Kneeanderthul 的头像

Kneeanderthul1 个月前

Nice nice!!! Did something similar with a heavy harness and a SLM Tower of Hanoi at 100 disk Congratulations on the release

(((Palee))) 的头像

(((Palee)))1 个月前

This is really cool!

ORQANTA 的头像

ORQANTA1 个月前

perfect!

Günter Klambauer 的头像

Günter Klambauer1 个月前

Great visualization! Since you seem to be aware of HRM, TRM, EqR, please also check SE-RRM ( , ICML2026)

Nathan Kochera 的头像

Nathan Kochera1 个月前

Excited to take a look at the paper. Congrats!

Cyan Ding 的头像

Cyan Ding1 个月前

very cool work!

Arnas Uselis 的头像

Arnas Uselis1 个月前

Very cool!! How does it compare to ELF (language flows) conceptually? (Apart from the fact that this isn't language-based)

Alec Helbling 的头像

Alec Helbling1 个月前

Thanks! The core difference is that ELF operates no embeddings of tokens from some external encoder (like T5). In our setting, we use a one hot encoding similar to Flow Language Models. This makes sense for problems like sudoku where digits are interchangeable and there is no inherent semantics differentiating the different vocabulary elements.

Shaurya 的头像

Shaurya1 个月前

Intresting if this is so fast in inference can we distill large models after training into FRMs?

snow 的头像

snow1 个月前

Is it generalized or only works for sudoku problem ? Did you try other richer problems ?

Immanuel Kan’t 的头像

Immanuel Kan’t1 个月前

@adamnemecek1 is this Energy Based Model?

Formiga 的头像

Formiga1 个月前

Where do you see this going when dealing with continuous problem such as robotics policies?

Lord of the Mountains 的头像

Lord of the Mountains1 个月前

Can you describe the residual in your DiT? I'm waiting for you code to drop so I understand what happened there. I'm comparing to which fully depends on carrying the hidden state.

Alec Helbling 的头像

Alec Helbling1 个月前

We take the logits, the un-decoded output, of the last denoising pass as an additional conditioning signal.

Lord of the Mountains 的头像

Lord of the Mountains1 个月前

Just finished testing FRM, Stage A -> Stage B (FPF), I found that it starts with some degradation before it picks back up. Carried information mechanism doesn't appear to deliver (self-conditioning on the model's own predictions). Now just waiting for you code to confirm diff

Talgat Daulbaev 的头像

Talgat Daulbaev1 个月前

Hi! Have you run any experiments on ARC-AGI?

Ankush Singal 的头像

Ankush Singal1 个月前

Wrote an article on it Flow Reasoning Models: Turning Flows Into Efficient Recurrent Reasoners

Juan Pablo Gallego 的头像

Juan Pablo Gallego1 个月前

Beautiful! Have you considered using FPF’s convergence signal as a learned per-token error/value-of-compute map? you could halt easy cases, refine unresolved regions, and replay confidently wrong low residual states? It could make recurrence both adaptive and locally targeted.

♠️🗝♦️Omicron's Domain♣️🧩❤️ 的头像

♠️🗝♦️Omicron's Domain♣️🧩❤️1 个月前

What does the edge it's following represent?

Arnaud Deza 的头像

Arnaud Deza1 个月前

Wow! Have you tested any other discrete optimization problem than sudoku? I.e doing this for maxcut or vertex cover etc I.e neural combinatorial optimization

TTD 🇮🇩 的头像

TTD 🇮🇩1 个月前

Agreed

Oliver S 的头像

Oliver S1 个月前

Great research! The self-conditioning loop mirrors how good science works: propose, verify, falsify, refine. Love seeing that built into the architecture itself! Any plans to test it beyond Sudoku?

Sanket Desai 的头像

Sanket Desai1 个月前

I built these last month lmao. Nice research. Did you check out the block version of this?

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With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.

RoboPapers

85,927 次观看 • 11 个月前