Thinking Without Talking: How SynAdapt Lets LLMs Reason in Silence
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. ...