Efficient Reasoning

When it comes to reasoning, bigger isn’t always better. Large language models (LLMs) often produce unnecessarily long chains of thought, burning through tokens — and budgets — even for simple problems. While fixed token limits during training can force brevity, they also rob models of the chance to first explore and then compress their reasoning. A new study, Train Long, Think Short, proposes a smarter path: curriculum learning for length control. Instead of a one-size-fits-all cap, the model starts with a generous token budget, learns robust reasoning strategies, and then gradually adapts to shorter limits over time. The result is a model that solves complex tasks with fewer tokens, without losing accuracy. ...

When large language models (LLMs) reason step-by-step using Chain-of-Thought (CoT) prompting, they think out loud. That verbosity improves accuracy—but it’s also a luxury many applications can’t afford. From real-time voice assistants to robotics, excessive token generation slows everything down. The result is a fundamental bottleneck: performance versus efficiency. The paper SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought offers a clever solution. Rather than generating verbose natural language steps, SynAdapt trains LLMs to reason silently, using internal vectors called synthetic continuous CoT (CCoT). And for harder problems—where silence isn’t enough—it smartly reroutes the model back into verbal reasoning mode. This hybrid, adaptive strategy achieves the best of both worlds. ...