A PyTorch Library for Meta-learning Research
Active LR optimizer for AdamW and RAdam
Evolver is a tool based on the formulation of the automatic configuration and design of multi-objective metaheuristics as a multi-objective optimization problem.
Autonomous, resumable state machine for continuous ML meta-optimization. Orchestrates background ideation, code materialization, and remote queue execution via specialized subagents.
A runtime substrate that turns an agent's execution into a reversible, Git-like trace, so meta-agents can observe, fork, replay, and revert any run. Couples agent and environments in a copy-on-write fork ~5x faster than docker commit, with ~95% KV-cache reuse on replay. Framework built for meta-agents to supervise, optimize, and train other agents
Add a description, image, and links to the meta-optimization topic page so that developers can more easily learn about it.
To associate your repository with the meta-optimization topic, visit your repo's landing page and select "manage topics."