Capabilities · Trend
Science of AI Training Dynamics
New mechanistic analyses of AdamW training dynamics and Weibull weight distributions advance the scientific understanding of how transformer models evolve during training.
Forecast horizon
Connections
Connections · 2
How this node ties into the rest of the map, and the evidence behind each link.
to · enables 4/10
Zero-Knowledge Proof Verification of AI Training Compute A scientific understanding of training dynamics is a prerequisite for designing verifiable training specifications used in ZK proof architectures.
+4 growth from · informs 4/10
LLM Probabilistic Reasoning Limitations Systematic probabilistic reasoning failures in LLMs motivate studying training dynamics to understand why these biases emerge and persist.
+4 growthSignal sources
Signal sources
Dated facts from primary sources in this direction.
Task horizon doubling Mar 2025
The length of software tasks AI agents can do autonomously at 50% reliability has doubled about every 7 months — and since 2024 closer to every ~3 months.
METR → Benchmarks saturating Apr 2025
In one year scores rose by 18.8, 48.9 and 67.3 points on MMMU, GPQA and SWE-bench; real-world software solve rate jumped from 4.4% to 71.7%.
Stanford HAI — AI Index 2025 → Autonomous coding 2025–2026
On SWE-bench Verified (500 real GitHub issues), autonomous coding agents reached ~80–86% by late 2025, up from under 50% in early 2025.
Epoch AI →