REFINE: A New Reinforcement Learning Framework Improves Long-Context Modeling with Next-Sequence Prediction

A recent paper introduces REFINE (Reinforced Fast Weights for Next-Sequence Prediction), a novel reinforcement learning framework designed to enhance long-context modeling in fast weight architectures. Traditional models rely on next-token prediction (NTP), which the authors argue is suboptimal for maintaining semantic coherence over long sequences. REFINE instead uses a next-sequence prediction (NSP) objective, evaluating the model's ability to predict multi-token continuations. The system identifies high-entropy token positions to generate rollouts and assigns rewards based on semantic similarity to the ground truth. Experimental results show significant performance gains across tasks like question answering and information retrieval, with the method proving effective from mid-training to test-time adaptation.