Technology

Traditional autoregressive language models generate text sequentially, one token at a time, leading to slower outputs with limited coherence and quality.

Diffusion models are an alternative approach. Instead of direct prediction, they iteratively refine noise, enabling faster generation, dynamic error correction, and greater control. This makes them particularly effective for editing tasks, including in math and code.

https://github.com/ML-GSAI/LLaDA

Here are the PRs that are created entirely by AI:

• https://github.com/dotnet/runtime/pull/115762
• https://github.com/dotnet/runtime/pull/115743
• https://github.com/dotnet/runtime/pull/115733
• https://github.com/dotnet/runtime/pull/115732

Devs are talking to the AI trying to get it to fix some mistakes. It's so cringe.

the goal is to have an agent that can:

• Understand a complex problem description.
• Generate initial algorithmic solutions.
• Rigorously test its own code.
• Learn from failures and successes.
• Evolve increasingly sophisticated and efficient algorithms over time.

https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/AlphaEvolve.pdf

https://www.youtube.com/watch?v=zsTNoEbOVUE

https://archive.ph/uqL6o

https://archive.ph/F6Dqh

MiMo-7B, a series of reasoning-focused language models trained from scratch, demonstrating that small models can achieve exceptional mathematical and code reasoning capabilities, even outperforming larger 32B models. Key innovations include:

• Pre-training optimizations: Enhanced data pipelines, multi-dimensional filtering, and a three-stage data mixture (25T tokens) with Multiple-Token Prediction for improved reasoning.
• Post-training techniques: Curated 130K math/code problems with rule-based rewards, a difficulty-driven code reward for sparse tasks, and data re-sampling to stabilize RL training.
• RL infrastructure: A Seamless Rollout Engine accelerates training/validation by 2.29×/1.96×, paired with robust inference support. MiMo-7B-RL matches OpenAI’s o1-mini on reasoning tasks, with all models (base, SFT, RL) open-sourced to advance the community’s development of powerful reasoning LLMs.

an in-depth discussion of mimo-7b https://www.youtube.com/watch?v=y6mSdLgJYQY