Opening — Why this matters now Speculative decoding has quietly become one of the most important efficiency tricks in large language model inference. It promises something deceptively simple: generate multiple tokens ahead of time with a cheap draft model, then let the expensive model verify them in parallel. Fewer forward passes, lower latency, higher throughput. ...
Opening — Why this matters now The AI industry has spent the last three years chanting a single mantra: more data, bigger models. It worked—until it didn’t. Performance gains are slowing, training costs are ballooning, and regulators are starting to ask uncomfortable questions about memorization, leakage, and data provenance. The paper you just uploaded steps directly into this tension and makes a slightly heretical claim: what we remove from training data may matter more than what we add. ...
Opening — Why this matters now Large reasoning models like GPT-5 and s1.1-32B can solve Olympiad-level problems — but they’re computationally gluttons. Running them for every query, from basic arithmetic to abstract algebra, is like sending a rocket to fetch groceries. As reasoning models become mainstream in enterprise automation, the question is no longer “Can it reason?” but “Should it reason this hard?” ...
Cut the Fluff: Leaner AI Thinking When it comes to large language models (LLMs), brains aren’t the only thing growing—so are their waistlines. As AI systems become increasingly powerful in their ability to reason, a hidden cost emerges: token bloat, high latency, and ballooning energy consumption. One of the most well-known methods for boosting LLM intelligence is Chain-of-Thought (CoT) reasoning. CoT enables models to break down complex problems into a step-by-step sequence—much like how humans tackle math problems by writing out intermediate steps. This structured thinking approach, famously adopted by models like OpenAI’s o1 and DeepSeek-R1 (source), has proven to dramatically increase both performance and transparency. ...