From Exploration to Exploitation: A Two-Stage Entropy RLVR Approach for Noise-Tolerant MLLM Training

The recent publication titled 'From Exploration to Exploitation: A Two-Stage Entropy RLVR Approach for Noise-Tolerant MLLM Training' introduces a novel methodology aimed at enhancing the training of Multimodal Large Language Models (MLLMs) through a two-stage entropy optimization process. This method is particularly relevant in scenarios where high-quality labeled data is scarce and often contaminated with noise, which can lead to inaccurate model predictions. By first maximizing token-level entropy during the exploration phase, the model is encouraged to generate diverse outputs, thereby preventing premature convergence on incorrect labels. As training progresses, the method shifts to minimizing entropy, which helps the model produce more confident and deterministic outputs. This phased strategy not only improves noise tolerance but also refines prediction accuracy, consistently outperforming previous approaches. The implications of this research are profound, as they provide a pathwa…