🤖 Inference-Time Scaling for Generalist Reward Modeling
Impossible d'ajouter des articles
Échec de l’élimination de la liste d'envies.
Impossible de suivre le podcast
Impossible de ne plus suivre le podcast
🤖 Inference-Time Scaling for Generalist Reward Modeling
-
Lu par :
-
De :
À propos de ce contenu audio
This paper explores enhancing reward modeling (RM) for large language models (LLMs) by improving inference-time scalability. The authors introduce Self-Principled Critique Tuning (SPCT), a novel learning method that encourages RMs to generate their own guiding principles and accurate critiques through online reinforcement learning. Their approach, embodied in the DeepSeek-GRM models, utilizes pointwise generative reward modeling for greater flexibility. By employing parallel sampling and a meta RM to refine the reward voting process, they demonstrate significant improvements in the quality and scalability of their GRMs across various benchmarks. Notably, inference-time scaling with their method shows competitive or superior performance compared to simply increasing model size.
Vous êtes membre Amazon Prime ?
Bénéficiez automatiquement de 2 livres audio offerts.Bonne écoute !