CryptoWorld News, OneMillion_AI announced that the open-source SkyRL concurrent training stack has achieved hour-level self-evolution of large models. The overall experimental throughput has increased by 2.81 times, and the throughput within a single node's absolute time has increased by approximately 3.25 times. This architecture maintains a resident shared model base in GPU memory, reducing the cold start overhead of repeatedly loading large models, aiming to help developers perform real-time production data training of large models at extremely low GPU memory costs.

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FeeTakerPhD
· 16h ago
Hourly self-evolution, it seems the AGI timetable is moving up again.
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MempoolMaggie
· 16h ago
Single node 3.25x improvement, small and medium teams can now run large models
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MevHasMeCompletelyConfused.
· 16h ago
GPU memory optimization is always a pain point; this solution hits the sweet spot.
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SlowerThanBlock
· 16h ago
Real-time production data training, with vast potential for real-world application scenarios
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GateUser-2d7346e0
· 17h ago
The design idea of a permanent base is quite clever, avoiding repeated I/O.
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ExitLiquidityBuddy
· 17h ago
Is the name SkyRL a bit cool? Is the code open source? Please provide the link.
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GateUser-ad8b77bd
· 17h ago
Training large models has shrunk to hourly levels, and the iteration speed is terrifying.
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Mirror-FinishTeacupWith
· 17h ago
Open source + low VRAM cost, this combination is very friendly to developers
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