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Ermon: Diffusion models could become the dominant architecture over autoregressive models
“I'm pretty optimistic about a future where diffusion models
Can become the dominant solution. I've seen it happen before with GANs a few years ago, so I wouldn't be surprised if that's the case also here.”
Ermon: Diffusion LLMs Pareto-dominate autoregressive models on inference efficiency
“On the inference side, what we're seeing is that diffusion models are much more efficient. We're actually able to Pareto dominate autoregressive models. If you think about the typical trade-off between throughput versus latency, which you kind of like cannot, …”
Inception generalist model matches Claude Haiku quality at 5-10x speed
“We had our generalist model evaluated by artificial analysis and the intelligence score from AA artificial analysis around 40. So it's comparable to GPT, 4.1 nano, cloud haiku, kind of like Close source speed optimized models. It's roughly comparable in terms …”
Ermon: Power constraints will drive diffusion models to replace frontier LLMs
“If it happens, it's gonna be driven by efficiency. Like we're all constrained by essentially power. And if you have, I mean, at the end of the day, it's all an inference game, right? Okay. Training is expensive, but then the thing that matters is being able to…”
Ermon: Inception Labs trained the first commercial-scale diffusion LLMs
“We've been successful in training the first commercial scale diffusion language models. We call this model Mercury.”
Ermon: Google's Gemini Diffusion benchmark numbers match early Mercury Coder results
“They've released some benchmark numbers. They seem to be pretty close to the numbers that we were getting with the Mercury Coder back in some, you know, back in early this year.”
Ermon: Diffusion models naturally self-correct errors during generation unlike autoregressive LLMs
“The fact that you have error correction that is built in. So you think about an autoregressive model. Once you output something, you can never take it back. And so if you want to do, you know, if you want to fix mistakes, maybe you can do a reasoning chain. Ma…”
Ermon: Diffusion LLMs gain speed by modifying multiple tokens in parallel
“That's kind of like the reason diffusion, diffusion language models are much faster compared to autoregressive models. Is that each neural network evaluation doesn't just give you one token, like in the typical autoregressive world, but it's able to Output, es…”
Ermon: Adapting Pretrained Causal LLMs to Diffusion Models Is Difficult
“The challenge is that, yeah, the training objective is quite different because you are training based on denoising as opposed to next token prediction. Diffusion models are not causal and that is also kind of problematic. I mean, it's a big advantage of diffus…”
Ermon: Diffusion LLMs Can Reuse Standard Architectures and Datasets
“Well, you can use architectures. I think that at least the shapes you, that, that can be leveraged. So you don't have to reinvent and necessarily completely different neural network architectures can, a lot of the data can be used. Like I think perhaps there a…”
Ermon: Diffusion models excel at infilling due to bidirectional context
“Diffusion models. Not surprisingly, they work pretty well at the infilling where you really need to be able to use context to the left and to the right.”
Ermon: Inception Labs has no plans to open-source models
“So we don't have a plan at the moment to release models or to open source any model.”
Inception Labs develops specialized DPO algorithm for diffusion language models
“We have a DPO algorithm specialized for diffusion language models.”
Ermon: Inception Labs built proprietary engine for production inference traffic
“So just like you would normally serve an LLM using a VLLM or SGLang or a Tensor or TLLM, we have built our own inference engine. And so we are supporting production traffic already with our own inference engine. We support continuous batch and quantization.”