Tag
This paper addresses the 'Lost in Conversation' problem where LLMs struggle with information revealed across multiple turns. It proposes a scalable sharding pipeline to create multi-turn training data from single-turn QA datasets and uses reinforcement learning with verifiable rewards to train a memory-augmented policy that maintains a compact rolling memory, improving multi-turn reasoning accuracy and generalizing zero-shot to harder tasks.