Cites: MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

5 citation edges inside this catalog. A title match is weaker evidence than an identifier match. Zero entries does not mean no citations on the web.

• Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
  arxiv:2006.16236 · evidence: title
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
  arxiv:2402.03300 · evidence: id
• DAPO: An Open-Source LLM Reinforcement Learning System at Scale
  arxiv:2503.14476 · evidence: id
• Understanding R1-Zero-Like Training: A Critical Perspective
  arxiv:2503.20783 · evidence: id
• Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
  arxiv:2506.01939 · evidence: id