The field of large language models is moving towards improving the efficiency and accuracy of reasoning tasks. Recent developments have focused on accelerating inference and generation processes, with a particular emphasis on speculative decoding and self-speculative approaches. These methods aim to reduce the computational cost and latency associated with traditional autoregressive decoding, while maintaining or even improving the quality of the generated responses. Notable advancements include the introduction of novel attention mechanisms, adaptive drafting strategies, and dynamic routing techniques, which have been shown to achieve significant speedups and improvements in accuracy. Some particularly noteworthy papers in this area include: SpecPV, which achieves up to 6x decoding speedup over standard autoregressive decoding with minor degradation. Arbitrage, which reduces inference latency by up to 2x at matched accuracy by dynamically routing generation based on the relative advantage between draft and target models. Plantain, which yields an ~6% improvement in pass@1 across several challenging math reasoning and coding benchmarks, while reducing time-to-first-response by over 60% relative to think-then-answer baselines.