Introspective Diffusion Language Models
introspective-diffusion.github.io
Introspective Diffusion Language Models
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Re: Introspective Diffusion Language Models
#2Is anyone here experimenting seriously with Diffusion for text generation? I’d love to learn about your experiences!
Re: Introspective Diffusion Language Models
#3Is anyone here experimenting seriously with Diffusion for text generation? I’d love to learn about your experiences!
I have. It requires a distinct intuition compared to a normal language model. Very well suited to certain problems.
Re: Introspective Diffusion Language Models
#4Is anyone here experimenting seriously with Diffusion for text generation? I’d love to learn about your experiences!
https://www.inceptionlabs.ai/
This startup seems to have been at it a while.
From our look into it - amazing speed, but challenges remain around time-to-first-token user experience and overall answer quality.
Can absolutely see this working if we can get the speed and accuracy up to that “good enough” position for cheaper models - or non-user facing async work.
One other question I’ve had is wondering if it’s possible to actually set a huge amount of text to diffuse as the output - using a larger body to mechanically force greater levels of reasoning. I’m sure there’s some incredibly interesting research taking place in the big labs on this.
Re: Introspective Diffusion Language Models
#5Re: Introspective Diffusion Language Models
#6Is anyone here experimenting seriously with Diffusion for text generation? I’d love to learn about your experiences!
https://www.inceptionlabs.ai/ This startup seems to have been at it a while. From our look into it - amazing speed, but challenges remain around time-to-first-token user experience and overall answer quality. Can absolutely see this working if we can get the speed and accuracy up to that “good enough” position for cheaper models - or non-user facing async work. One other question I’ve had is wondering if it’s possibl…
The overall speed rather than TTFT might start to be more relevant as the caller moves from being a human to another model.
However quality is really important. I tried that site and clicked one of their examples, "create a javascript animation". Fast response, but while it starts like this
``` Below is a self‑contained HTML + CSS + JavaScript example that creates a simple, smooth animation: a colorful ball bounces around the browser window while leaving a fading trail behind it.
JavaScript Bounce Animation body, html { margin: 0; padding: 0;
```
the answer then degrades to
``` radius: BALL_RADIUS, color: BALL_COLOR, traivD O] // array of previous {x,y} positions }; ```
Then more things start creeping in
``` // 3⃣ Bounce off walls if (ball.G 0 ball.radius _7{nas.width) { ball.vx *= -1; ibSl.x = Math.max(ball.radius, Math.min(ball.x, canvbbF4idth - ball.radius)); } if
```
and the more it goes on the worse it gets
``` Ho7 J3 Works 0 Atep | Description | ```
and
``` • prwrZ8}E6on 5 jdF wVuJg Ar touc> 2ysteners ,2 Ppawn \?) balls w>SFu the 8b$] cliM#]9 ```
This is for the demo on the front page, so I expect this is a pretty good outcome compared to what else you might ask.
Re: Introspective Diffusion Language Models
#7Is anyone here experimenting seriously with Diffusion for text generation? I’d love to learn about your experiences!
It's being explored right now for speculative decoding in the local-LLM space, which I think is quite interesting as a use-case
https://www.emergentmind.com/topics/dflash-block-diffusion-f...
Re: Introspective Diffusion Language Models
#8Earlier quoted context omitted.
https://www.inceptionlabs.ai/ This startup seems to have been at it a while. From our look into it - amazing speed, but challenges remain around time-to-first-token user experience and overall answer quality. Can absolutely see this working if we can get the speed and accuracy up to that “good enough” position for cheaper models - or non-user facing async work. One other question I’ve had is wondering if it’s possibl…
The overall speed rather than TTFT might start to be more relevant as the caller moves from being a human to another model. However quality is really important. I tried that site and clicked one of their examples, "create a javascript animation". Fast response, but while it starts like this ``` Below is a self‑contained HTML + CSS + JavaScript example that creates a simple, smooth animation: a colorful ball bounces a…
Weird; I clicked through out of curiosity and didn't get any corruption of the sort in the end result.
I also asked it some technical details about how diffusion LLMs could work and it provided grammatically-correct plausible answers in a very short time (I don't know the tech to say if it's correct or not).
Re: Introspective Diffusion Language Models
#9If I’m reading this right, this is pretty wild. They turned a Qwen autoregressor into a diffuser by using a bunch of really clever techniques, and they vastly outperform any “native diffuser,” actually being competitive with the base model they were trained from. The obvious upside here is the massive speedup in generation.
And then through a LoRA adapter, you can ground the diffuser on the base model’s distribution (essentially have it “compare” its proposals against what the base model would’ve generated), which effectively means: exact same byte-for-byte output for the same seed, just roughly twice as fast (which should improve even more for batched tasks).
I’m not an expert, more of a “practicing enthusiast,” so I might be missing something, but at first glance, this reads super exciting to me.
Re: Introspective Diffusion Language Models
#10Is anyone here experimenting seriously with Diffusion for text generation? I’d love to learn about your experiences!
I've been playing with a Swift implementation of a diffusion language model (WeDLM), but performance is not yet acceptable and it still generates roughly from left-to-right like a language model (just within a sliding window rather than strictly token-by-token... but that doesn't matter when the sliding window is only like 16 tokens.)