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Research 🇺🇸

Beyond Standard LLMs: Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers

The article by Sebastian Raschka explores alternatives to standard autoregressive transformer LLMs, including linear attention hybrids (e.g., MiniMax-M1, Qwen3-Next, DeepSeek V3.2, Kimi Linear), text diffusion models, code world models, and small recursive transformers. It discusses the revival of linear attention mechanisms and challenges, such as MiniMax reverting to standard attention in its M2 model due to poor performance on reasoning tasks.

Moonshot AI

MiniMax

Alibaba/Qwen

DeepSeek

Google/DeepMind

Mistral

State of LLMs in 2025: Progress, Challenges, and Predictions

In 2025, LLM development was dominated by reasoning models using reinforcement learning with verifiable rewards (RLVR) and the GRPO algorithm, sparked by DeepSeek R1. Key trends include a focus on inference-time scaling, MoE architectures, and efficiency tweaks like Gated DeltaNets. Academic research highlighted GRPO variants with improvements such as zero gradient filtering and token-level loss.

DeepSeek

OpenAI

Alibaba/Qwen

Moonshot AI

NVIDIA

Google/DeepMind

Sebastian Raschka27.07 · 17:03

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