RLLM explores learning through interaction in small language-related environments.
One setup is a referential game with single-token messages. Another uses a one-room TextWorld task with a key and a door. Supporting models explore policies, learned environment dynamics, and tree-based planning.
The intent is to study the learning loop before expanding the environment or language. The environments keep actions and outcomes limited enough to inspect, while comparisons between representations and planning choices remain experimental.