{"slug": "minimax-m1-scaling-test-time-compute-with-lightning-attention-interactive-visual", "title": "MiniMax-M1: Scaling Test-Time Compute with Lightning Attention — interactive visual explainer | Rudrite Research", "summary": "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.", "body_md": "# MiniMax-M1: Scaling Test-Time Compute with Lightning Attention\n\nClip the importance weight, not the update — efficient RL for a hybrid-attention reasoning model.\n\nMiniMax · arXiv 2025 · Reasoning & RL. [Read the paper ↗](https://arxiv.org/abs/2506.13585)\n\nA 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.\n\n## Questions\n\n- What is MiniMax-M1: Scaling Test-Time Compute with Lightning Attention?\n- Clip the importance weight, not the update — efficient RL for a hybrid-attention reasoning model.\n- Who published MiniMax-M1: Scaling Test-Time Compute with Lightning Attention, and where?\n- MiniMax — arXiv 2025 (arXiv:2506.13585).\n- Where can I find a visual explainer of MiniMax-M1: Scaling Test-Time Compute with Lightning Attention?\n- Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source.\n\n## Related explainers\n\n[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)", "url": "https://wpnews.pro/news/minimax-m1-scaling-test-time-compute-with-lightning-attention-interactive-visual", "canonical_source": "https://research.rudrite.com/cispo", "published_at": "2026-06-13 00:00:00+00:00", "updated_at": "2026-06-14 18:18:08.656384+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-products"], "entities": ["MiniMax", "arXiv", "Rudrite Research"], "alternates": {"html": "https://wpnews.pro/news/minimax-m1-scaling-test-time-compute-with-lightning-attention-interactive-visual", "markdown": "https://wpnews.pro/news/minimax-m1-scaling-test-time-compute-with-lightning-attention-interactive-visual.md", "text": "https://wpnews.pro/news/minimax-m1-scaling-test-time-compute-with-lightning-attention-interactive-visual.txt", "jsonld": "https://wpnews.pro/news/minimax-m1-scaling-test-time-compute-with-lightning-attention-interactive-visual.jsonld"}}