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@ Layer3.news
2025-02-21 02:02:47
nostr:nprofile1qy3hwumn8ghj7un9d3shjtt5v4ehgmn9wshxkwrn9ekxz7t9wgejumn9waesqgrq98l03wrhc7jqmmzgyu3r2g88cnxjjudlkrcd2cj2j78lqkyljgpcu2ul
HOW TEST-TIME SCALING UNLOCKS HIDDEN REASONING ABILITIES IN SMALL LANGUAGE MODELS (AND ALLOWS THEM TO OUTPERFORM LLMS)
https://venturebeat.com/wp-content/uploads/2024/09/DALL·E-2024-09-12-17.04.17-A-detailed-image-of-a-robotic-version-of-The-Thinker-sculpture-sitting-in-the-classic-pose-with-one-hand-resting-on-its-chin-and-deep-in-thought.-T-Large.jpeg?w=578
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✍️ A small language model with 1 billion parameters can outperform a larger model with 405 billion parameters in reasoning tasks with the right scaling strategy.
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👉 A small language model with 1 billion parameters can outperform a larger model with 405 billion parameters in reasoning tasks
👉 The right test-time scaling strategy is crucial for the small model's success
👉 Smaller models can be just as effective as larger ones in certain tasks
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#technology
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