Practical Solutions and Value of BOND: A Novel RLHF Method
Enhancing Language Generation Quality
Reinforcement learning from human feedback (RLHF) is crucial for ensuring quality and safety in language and learning models (LLMs). State-of-the-art LLMs like Gemini and GPT-4 undergo three training stages: pre-training on large corpora, supervised fine-tuning, and RLHF to refine generation quality. Best-of-N sampling is a practical approach to enhance generation quality, effectively balancing reward and computational cost.
Efficient RLHF Algorithm
Best-of-N Distillation (BOND) is an innovative RLHF algorithm designed to replicate the performance of Best-of-N sampling without its high computational cost. It aligns the policy’s output with the Best-of-N distribution using Jeffreys divergence, enhancing KL-reward trade-offs and benchmark performance.
Reducing Computational Demands
BOND focuses on investing resources during training to reduce inference-time computational demands, aligning with principles of iterated amplification. It efficiently achieves the benefits of Best-of-N sampling, reducing the computational overhead.
Practical Implementation with Minimal Sample Complexity
J-BOND is a practical implementation of the BOND algorithm designed for fine-tuning policies with minimal sample complexity. It outperforms traditional RLHF methods, demonstrating effectiveness and better performance without needing a fixed regularization level.
Improving KL-Reward Pareto Front
BOND improves the KL-reward Pareto front and outperforms state-of-the-art baselines, demonstrating its effectiveness in experiments on abstractive summarization and Gemma models.
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