Scaling laws
Technique · Training · 2 items since May 2026
Predictable relationships between performance and model size, data, or compute.
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- Oct 9
MIT's fault-tolerant foundation models
LLMs trained with the same hardware faults they will meet at inference grow more error-resilient as they scale, following a modified neural scaling law that points to running inference on low-energy, unreliable hardware for large energy savings.
- May 6
ScaleLogic finds a power law for RL compute
RL training compute scales as a power of reasoning depth (R² > 0.99), with the exponent rising from 1.04 to 2.60 as logical expressiveness grows; easy-to-hard curricula improve efficiency and lift downstream math and reasoning benchmarks by up to 10.66 points.