Bootstrapping Composer with autoinstall

Composer's development process leverages past model versions to enhance future training, with a focus on refining environment setups crucial for reinforcement learning (RL). To address the inefficiencies caused by faulty environments, Composer employs an autoinstall system, which uses previous Composer models to automatically configure working RL environments from unconfigured repository checkouts. This process mimics production Cursor systems by automating the setup, package installation, and configuration of cloud environments. Autoinstall operates in two stages: first, setting goals and proposing commands, and second, executing these commands to ensure a runnable environment. This system was tested on complex projects like the Celo blockchain, handling dependencies and creating mock setups when necessary. The improved environment setup capabilities of Composer 2, which outperformed its predecessor on environment setup benchmarks, suggests that such bootstrapping methods will significantly enhance future model training processes.