Learning to Solve, Forgetting to Retain: Correct-Set Turnover in RLVR
Researchers have identified a phenomenon called "correct-set turnover" in reinforcement learning with verifiable rewards (RLVR) for large language models. This issue causes models to forget previously solved problems as they are trained on new ones. To combat this, a new retention-aware review mechanism called "Remind" has been proposed. Remind aims to explicitly optimize for retention alongside acquisition by periodically reintroducing solved problems, demonstrating improved performance across various benchmarks and modalities. AI
IMPACT Addresses a critical limitation in LLM training, potentially leading to more robust and reliable models across various tasks.