Map the field around what you are building, then decide where to focus.

RRLM Science Map

Recursive Reinforcement Language Models: an RL-trained recursive scaffold that decomposes, retrieves and routes over million-token corpora, matching a read-everything long-context oracle at a small and shrinking fraction of its compute. Core pieces: context-as-environment recursion (RLM), a corpus-privileged adversarial query generator that co-trains a cheap retriever on global/aggregative queries, a learned router over {retriever, symbolic index, oracle}, amortization (index once, answer many), and a taxonomy of mental operations used to diagnose and reward reasoning traces.