Jul 23, 08:17 AM

Google DeepMind Introduces BOND RLHF Method for Fine-Tuning Gemma 1.1 Models with 2B and 7B Parameters

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.

#Google DeepMind #Gemma

Written with ChatGPT (GPT-4o mini).

Sources

Vlad Ruso PhD@vlruso
2 years ago
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
Marktechpost AI Dev News ⚡@Marktechpost
2 years ago
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
Machine Learning@Memoirs
2 years ago
BOND: Aligning LLMs with Best-of-N Distillation. https://t.co/PigXap7BFb

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Google DeepMind Introduces BOND RLHF Method for Fine-Tuning Gemma 1.1 Models with 2B and 7B Parameters

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