Redemption

We built an OpenEnv reinforcement learning environment where an agent learns to act as an optimizer for training machine learning models. Instead of using fixed optimization rules like SGD or Adam, the agent observes training statistics such as gradients, loss values, and parameter norms, and decides how model weights should be updated. The environment simulates a training loop where each action modifies the update step, and rewards are based on improving loss reduction while maintaining stability during training. This allows the agent to learn adaptive optimization strategies that change depending on the model state and training phase. Our goal is to explore whether optimization itself can be learned through interaction with a training environment rather than relying on static mathematical formulas.