MiniMax-M1: Scaling Test-Time Compute with Lightning Attention — interactive visual explainer | Rudrite Research

MiniMax released MiniMax-M1, a hybrid-attention reasoning model that scales test-time compute using lightning attention, detailed in a 2025 arXiv paper. The model employs efficient reinforcement learning by clipping importance weights rather than updates. An interactive visual explainer of the paper is available online.

MiniMax-M1: Scaling Test-Time Compute with Lightning Attention Clip the importance weight, not the update — efficient RL for a hybrid-attention reasoning model. MiniMax · arXiv 2025 · Reasoning & RL. Read the paper ↗ https://arxiv.org/abs/2506.13585 A free, interactive, animated visual explainer of MiniMax-M1: Scaling Test-Time Compute with Lightning Attention — every exhibit computed from the real formulas, with verbatim quotes from the source. Questions - What is MiniMax-M1: Scaling Test-Time Compute with Lightning Attention? - Clip the importance weight, not the update — efficient RL for a hybrid-attention reasoning model. - Who published MiniMax-M1: Scaling Test-Time Compute with Lightning Attention, and where? - MiniMax — arXiv 2025 arXiv:2506.13585 . - Where can I find a visual explainer of MiniMax-M1: Scaling Test-Time Compute with Lightning Attention? - Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source. Related explainers DeepSeek-R1 /deepseek-r1 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models /chain-of-thought Training language models to follow instructions with human feedback /instructgpt Direct Preference Optimization: Your Language Model is Secretly a Reward Model /dpo DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models /deepseekmath Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters /test-time-compute Constitutional AI: Harmlessness from AI Feedback /constitutional-ai DAPO: An Open-Source LLM Reinforcement Learning System at Scale /dapo