What is recorded here?
Combined efficient hybrid-attention reasoning with CISPO reinforcement learning, which clips importance-sampling weights, and trained on reasoning and software-environment tasks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Original 40K/80K checkpoints are one training contribution. Reported rental cost covers the stated RL run, not pretraining, research or total development. Relative clipping and compute benefits are empirical, not universal optimization guarantees.
Read the original work
- MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
- MiniMax: CISPO and hybrid-attention reasoning training — official company record
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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English catalogue narrative
MiniMax: CISPO and hybrid-attention reasoning training
Combined efficient hybrid-attention reasoning with CISPO reinforcement learning, which clips importance-sampling weights, and trained on reasoning and software-environment tasks.
Scientific scope
Original 40K/80K checkpoints are one training contribution. Reported rental cost covers the stated RL run, not pretraining, research or total development. Relative clipping and compute benefits are empirical, not universal optimization guarantees.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.