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SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
arXiv – CS AI|Dachuan Shi, Abedelkadir Asi, Keying Li, Xiangchi Yuan, Leyan Pan, Wenke Lee, Wen Xiao||4 views
🤖AI Summary
Researchers introduce SwiReasoning, a training-free framework that improves large language model reasoning by dynamically switching between explicit chain-of-thought and latent reasoning modes. The method achieves 1.8%-3.1% accuracy improvements and 57%-79% better token efficiency across mathematics, STEM, coding, and general benchmarks.
Key Takeaways
- →SwiReasoning dynamically switches between explicit and latent reasoning modes based on confidence estimates from entropy trends.
- →The framework addresses key challenges in latent reasoning including probability mass diffusion and overthinking problems.
- →Testing shows consistent 1.8%-3.1% accuracy improvements across different LLM families and scales.
- →Token efficiency improves by 57%-79% under constrained budgets, with larger gains as budgets tighten.
- →The solution is training-free, making it easily applicable to existing reasoning LLMs without retraining.
#llm#reasoning#ai-efficiency#machine-learning#natural-language-processing#token-optimization#research#arxiv
Read Original →via arXiv – CS AI
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