Reward-Guided Speculative Decoding for Efficient LLM Reasoning
Authors: name: Baohao Liao, name: Yuhui Xu, name: Hanze Dong, name: Junnan Li, name: Christof Monz, name: Silvio Savarese, name: Doyen Sahoo, name: Caiming Xiong

We introduce Reward-Guided Speculative Decoding (RSD), a novel framework
aimed at improving the efficiency of inference in large language models (LLMs).
RSD synergistically combines a lightweight draft model with a more powerful
target model, incorporating a controlled bias to prioritize high-reward
outputs, in contrast to existing speculative decoding methods that enforce
strict unbiasedness. RSD employs a process reward model to evaluate
intermediate decoding steps and dynamically decide whether to invoke the target
model, optimizing the trade-off between computational cost and output quality.
We theoretically demonstrate that a threshold-based mixture strategy achieves
an optimal balance between resource utilization and performance. Extensive
evaluations on challenging reasoning benchmarks, including Olympiad-level
tasks, show that RSD delivers significant efficiency gains against decoding
with the target model only (up to 4.4x fewer FLOPs), while achieving
significant better accuracy than parallel decoding method on average (up to
+3.5). These results highlight RSD as a robust and cost-effective approach for
deploying LLMs in resource-intensive scenarios.