Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Models

Understand

Reinforcement learning plays a crucial role in generative re-ranking scenarios due to its exploration-exploitation capabilities, but existing generative methods mostly fail to adapt to the dynamic entropy changes in model difficulty during list generation, making it challenging to accurately capture complex preferences.

• Given that language models have achieved remarkable breakthroughs by integrating reasoning capabilities, we draw on this approach to introduce a latent reasoning mechanism, and experimental validation demonstrates that this mechanism effectively reduces entropy in the model's decision-making process.
• Based on these findings, we introduce the Entropy-Guided Latent Reasoning (EGLR) recommendation model, which has three core advantages.
• First, it abandons the "reason first, recommend later" paradigm to achieve "reasoning while recommending", specifically designed for the high-difficulty nature of list generation by enabling real-time reasoning during generation.