[FranckDernoncou]

Paper: https://arxiv.org/abs/2605.12825 ; Code+models: https://github.com/chiennv2000/orthrus ; Disclosure: co-author.

Idea: Inject a trainable diffusion attention module into each layer of a frozen AR Transformer. Both heads share one KV cache. Diffusion head projects K=32 tokens in parallel; AR head verifies in a second pass and accepts the longest matching prefix. Output distribution is provably identical to the base model.

Results:

- Up to 7.8x TPF, ~6x wall-clock on MATH-500.

- 16% of params trained, <1B tokens, 24h on 8xH200.

- vs. diffusion LMs (Dream, Fast-dLLM-v2, SDAR, Mercury, Gemini Diffusion): they modify base weights and lose accuracy (Fast-dLLM-v2: -11 pts on MATH-500). Orthrus freezes the backbone; accuracy matches Qwen3-8B exactly.

- vs. Speculative Decoding (EAGLE-3, DFlash): no external drafter, no separate cache, zero TTFT penalty (no drafter to init/sync). KV overhead is O(1) (~4.5 MiB flat). Acceptance length on MATH-500: 11.7 vs. 7.9 (DFlash) vs. 3.5 (EAGLE-3).

- Single-step denoising beats multi-step (6.35 vs. 3.53 TPF). KL distillation beats CE on acceptance rate.

Limitations: strictly bounded by the frozen base model (inherits its biases, hallucinations, knowledge gaps); Qwen3-only evaluation; greedy + rejection sampling only.

[ilaksh]

Amazing. Is it possible to do this with Qwen 3.6 27B? Will it work with quants (I assume so)?

[sleepyeldrazi]

From a quick and shallow view of the paper, it looks very feasible (with a little tinkering ) to be adapted to qwen3.6 27B. The process looks somewhat similar to training a LoRA, or in a way distilling your own model so that a mini model learns how to imitate it, and you glue them. I might bite the bullet and rent a gpu to do it for 3.6 27b, as this will solve a lot of my problems.

[sleepyeldrazi]

Scratch that, I don't have that kind of money, and 3.5's architecture is a little more divergent from 3's, so it will be a bit less trivial. It does look possible, just not on a student's paycheck.

[Boranbruh]

There are websites that let you rent GPUs for cheap, such as QuickPod. Have you checked those P2P GPU rentals out?

[sleepyeldrazi]

My plan is to validate it first using qwen3.5 0.8B if it even works (as it has the same architecture as qwen3.6 27b, just scaled down a bit) on my 3090. If it does, I'll make a git about the process if anyone wants to use my approach, while I try to convince my uni to lend me h100s for a day.

[sleepyeldrazi]

If anyone is interested in watching my 0.8B experiments: https://orthrus.kokoham.com/ . The current code is here: https://git.kokoham.com/sleepy/qwen_orthrus .

The hard part was that the original Orthrus works with transformers, but 3.5(and 3.6) is Hybrid: 75% GatedDeltaNet + 25% GatedAttention. I am testing a trick that might make is work with the GatedDeltaNet, and dry runs are promising, but only a full train will reveal if it works. More information in the repo and on the site under the "What is this all about?" button.

Note: i may restart it or try different configs at different points, if the site is down there is probably some sort of result/conclusion in the repo.

[0-_-0]

3.6 already supports multi token generation AFAIK

[jbellis]

Yes, but not diffusion based, it's still doing token-at-a-time speculation.

[0-_-0]

I thought it can do multiple tokens at a time

[sleepyeldrazi]

Think of this as another way of achieving that. This theoretically has a higher ceiling of how much it can predict at a time. And more importantly is a lot more memory efficient during actual inference.

[regularfry]

There was a chart from the Unsloth folks posted to Reddit in the last couple of days which showed that the draft sweet spot for MTP was 2-3 tokens ahead depending on the quant. Thats not much, and I think this might do a lot better. The whole "provably identical distribution" thing is doing a lot of work in my head, and I don't think that's true of the MTP model in qwen's architecture.

[littlestymaar]

So, it's D-Flash but at each transformer layer and share the KV cache of the original model? Very smart!

[foobar10000]

Kindof yeah - predictivity is a question though for larger layers - when trying to scale this up. But yeah, this is a "95% predictor in latent space is a 7x improvement in speed if done right" approach.

[deflator]

I'm sure I don't understand all the technical aspects, but I do understand that this is frickin' cool. Nice work.

[jbellis]

Really cool work!

Does the training data budget scale with model size?

How would you compare the Gemma 4 draft model which is also integrated with the base kv cache?

[gkapur]

On the limitation side:

Do you think this would scale to larger transformer models with more parameters per layer?

How would this work with MOE models or sparse models?

[dot_treo]

Do you plan on releasing the training code?

[jbellis]

BTW the paper says

> Since only (Qdiff,Kdiff,Vdiff) are updated during training, the total number of trainable parameters is approximately 16% of the full model.

But the code defines q_proj_diff, k_proj_diff, v_proj_diff, and o_proj_diff, and it only matches 16% when you include the O term.