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🧠 AI🟢 BullishImportance 6/10
TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
arXiv – CS AI|Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya, Michael Gastpar, Ashok Vardhan Makkuva, Hyeji Kim|
🤖AI Summary
Researchers developed TERMINATOR, an early-exit strategy for Large Reasoning Models that reduces Chain-of-Thought reasoning lengths by 14-55% without performance loss. The system identifies optimal stopping points during inference to prevent overthinking and excessive compute usage.
Key Takeaways
- →TERMINATOR reduces Chain-of-Thought reasoning lengths by 14-55% across four challenging datasets while maintaining performance.
- →Large Reasoning Models often suffer from overthinking, spending excessive compute time after generating correct answers.
- →The system leverages first answer positions to predict optimal reasoning termination points.
- →Performance was validated on MATH-500, AIME 2025, HumanEval, and GPQA datasets.
- →TERMINATOR outperforms current state-of-the-art early stopping methods for LRMs.
#ai-optimization#chain-of-thought#inference-efficiency#model-performance#computational-cost#early-stopping#reasoning-models
Read Original →via arXiv – CS AI
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