BWhat changed
Combined efficient hybrid-attention reasoning with CISPO reinforcement learning, which clips importance-sampling weights, and trained on reasoning and software-environment tasks.
Scientific scope
Combined efficient hybrid-attention reasoning with CISPO reinforcement learning, which clips importance-sampling weights, and trained on reasoning and software-environment tasks. 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.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 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.
Contributors
- Source-listed authors and technical contributors · SCIENCE: 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.
- MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
- MiniMax: CISPO and hybrid-attention reasoning training — official company record
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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