Can Agent Skills Be Trained Like Neural Networks? SkillOpt Demonstrates Success

SkillOpt treats an agent’s Skill document as a trainable external state, applying classic deep‑learning tools such as epochs, batch size, learning rate and validation gating, and in experiments across 52 benchmark units it lifts GPT‑5.5 performance by an average of 23.5 points while enabling cross‑model and cross‑environment transfer with no additional inference cost.

Agent SkillCross-Model TransferDeep Learning Optimization

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Can Agent Skills Be Trained Like Neural Networks? SkillOpt Demonstrates Success

AI Frontier Lectures

Mar 21, 2025 · Artificial Intelligence

How ConFIG Eliminates Gradient Conflicts for Faster Multi‑Task Deep Learning

The paper introduces ConFIG (Conflict‑Free Inverse Gradients), a mathematically proven method that resolves gradient conflicts among multiple loss terms in physics‑informed neural networks, multi‑task learning, and continual learning, and its momentum‑based variant M‑ConFIG that further accelerates training while maintaining accuracy.

CONFIGDeep Learning OptimizationGradient Conflict

0 likes · 11 min read

How ConFIG Eliminates Gradient Conflicts for Faster Multi‑Task Deep Learning