#diffusion

r/MachineLearning did not reward this post for frontier performance. It took off because a 7.5M-parameter diffusion LM trained on tiny Shakespeare on an M2 Air made a usually intimidating idea feel buildable.

RAD-2 reframes diffusion-based driving planners as a generator-discriminator system, then adds reinforcement learning feedback where imitation-only training is weakest. The headline number is a 56% collision-rate drop versus strong diffusion planners, plus reported real-world deployment in complex urban traffic.

HN reacted fast because I-DLM is not selling faster text generation someday; it is claiming diffusion-style decoding can keep pace with autoregressive quality now. The thread quickly turned into a reality check on whether the 2.9x-4.1x throughput story can survive real inference stacks.

PyTorch said on April 8 that MXFP8 and NVFP4 quantization with Diffusers and TorchAO can cut diffusion latency on NVIDIA B200 GPUs, with NVFP4 reaching up to 1.68x speedups. The accompanying blog frames selective quantization and regional compilation as the practical recipe for better latency-memory tradeoffs.

A Reddit post in r/MachineLearning highlights a new MIT 2026 course on flow matching and diffusion models with lecture videos, mathematically self-contained notes, and coding exercises. The updated course expands into latent spaces, diffusion transformers, and discrete diffusion language models.

r/singularity pointed to Meituan's LongCat-Image-Edit-Turbo, a distilled open-source image editor that claims high-quality results in just 8 NFEs. The release pairs an Apache 2.0 Hugging Face model with a public arXiv report and community scrutiny over benchmark framing.

Inception Labs has released Mercury 2, the first production-ready diffusion language model for reasoning. Running at over 1,000 tokens per second on Blackwell GPUs, it is dramatically faster and cheaper than leading autoregressive competitors.