SwiReasoning is a decoding-time framework that lets a reasoning LLM decide when to think in latent space and when to write explicit chain-of-thought, using block-wise confidence estimated from entropy trends in next-token distributions. The method is training-free, model-agnostic, and targets Pareto-superior accuracy/efficiency trade-offs on mathematics and STEM benchmarks. Reported results show +1.5%–2.8% average accuracy improvements […]
The post SwiReasoning: Entropy-Driven Alternation of Latent and Explicit Chain-of-Thought for Reasoning LLMs appeared first on MarkTechPost.