Speculative Rejection for aligning LLM: Making Best-of-N work on 1 GPU instead of 32
Early rejection of low-quality LLM responses and dynamic batch pruning saves 32x GPU compute while maintaining quality.
📚 https://arxiv.org/abs/2410.20290
🎯 Original Problem:
Best-of-N decoding, while effective for aligning LLM outputs with human preferences, requires massive computational resources (16-32 GPUs) to generate multiple responses. This makes it impractical for real-world deployment despite its effectiveness.
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🛠️ Solution in this Paper:
• Introduces Speculative Rejection - a method that starts with large batch size and progressively rejects low-quality generations early
• Uses reward model to evaluate partial responses during generation
• Halts generations unlikely to achieve high final scores
• Dynamically reduces batch size to prevent memory exhaustion
• Requires minimal hyperparameter tuning (only rejection rate α)
• Works with any reward model and integrates with existing inference systems
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💡 Key Insights:
• Partial response scores strongly correlate with final response quality
• GPU memory is underutilized in early stages of generation
• Early rejection of unpromising responses saves computational resources
• Dynamic batch size adjustment prevents memory exhaustion
• Simple implementation yields substantial speedup
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📊 Results:
• Achieves 16-32x computational efficiency vs Best-of-N
• Requires only single GPU vs multiple GPUs for Best-of-N
• Maintains similar latency and generation quality
• Shows 85.5% token savings through early stopping
• Achieves higher win rates in GPT-4 evaluations (70.01% vs 63.04% baseline)
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