Sources
Loading...
Additional media
Loading...
DeepNewz, mobile.
People-sourced. AI-powered. Unbiased News.
Researchers at Google DeepMind have unveiled a new reinforcement learning from human feedback (RLHF) method called Best-of-N Distillation (BOND). This innovative algorithm is designed to fine-tune language models, specifically the Gemma 1.1 models with 2 billion and 7 billion parameters. BOND utilizes a novel Best-of-N Distillation approach to enhance model alignment with desired behaviors such as creativity and safety. The introduction of BOND aims to address key challenges in reward-based fine-tuning, facilitating the development of steerable multi-objective fine-tuning frameworks that align language policies with intended outcomes.
Researchers at Google Deepmind Introduce BOND: A Novel RLHF Method that Fine-Tunes the Policy via Online Distillation of the Best-of-N Sampling Distribution https://t.co/CdeN8iJqid #BOND #RLHF #AI #LanguageGeneration #BusinessTransformation #ai #news #llm #ml #research #ainew… https://t.co/JY3PnEYksJ
Researchers at Google Deepmind Introduce BOND: A Novel RLHF Method that Fine-Tunes the Policy via Online Distillation of the Best-of-N Sampling Distribution Researchers at Google DeepMind have introduced Best-of-N Distillation (BOND), an innovative RLHF algorithm designed to… https://t.co/RUUZUTTDot
BOND: Aligning LLMs with Best-of-N Distillation. https://t.co/PigXap7BFb
