stringnlplab/meta-agent is a repository containing the design and operating procedure for a meta agent that helps inspect and update the components of learning while an agent is learning. Rather than a framework that implements learning itself, it is a technique for delegating to a coding agent the work of examining the evidence in long experiments, choosing changes, and checking the results.
According to the README, the procedure starts by gathering existing learning recipes, experience from previous experiments, and currently observed evidence in order to form a hypothesis about the mechanism. It links the hypothesis to code and run-control items to test changes, and then checks again whether the results took effect. It says the procedure can be applied to pretraining, self-supervised learning, supervised learning, reinforcement learning, and agent learning, and it also includes scripts for experiment logging and trajectory export. It also presents an operating style that sets the boundaries of change authority and evaluation, reviews them periodically, and records the evidence. The repository's language is Python and its license is MIT.
However, what can be confirmed is the design of the meta agent and the procedure for operating experiments. No proprietary learning engine or quantitative evaluation results can be found in the README, so there is not enough basis to judge how much it actually improves learning performance. The scope is limited so that rewards or a user simulation environment are discussed only when they exist in the project in question. Therefore, while it does present a procedure applicable to multiple learning methods, it is closer to guidance for reviewing existing experiments and managing changes than to a tool whose improvement effect has been demonstrated.