Entropy-Aware Speculative Decoding Improves LLM Reasoning
Analysis
This paper introduces Entropy-Aware Speculative Decoding (EASD), a novel method to enhance the performance of speculative decoding (SD) for Large Language Models (LLMs). The key innovation is the use of entropy to penalize low-confidence predictions from the draft model, allowing the target LLM to correct errors and potentially surpass its inherent performance. This is a significant contribution because it addresses a key limitation of standard SD, which is often constrained by the target model's performance. The paper's claims are supported by experimental results demonstrating improved performance on reasoning benchmarks and comparable efficiency to standard SD.
Key Takeaways
- •EASD is a training-free enhancement to speculative decoding.
- •EASD uses entropy to identify and correct low-confidence predictions.
- •EASD can potentially surpass the performance of the target LLM.
- •EASD maintains efficiency comparable to standard speculative decoding.
“EASD incorporates a dynamic entropy-based penalty. When both models exhibit high entropy with substantial overlap among their top-N predictions, the corresponding token is rejected and re-sampled by the target LLM.”