INFUSER co-evolves a question generator and solver via influence-guided rewards, improving reasoning by over 20% on math benchmarks without curated data.
A lightweight merge module replaces token spans with surrogate embeddings, cutting Transformer sequence lengths by up to 40% with minimal accuracy loss and no retraining.