Inference-Time Compute Scaling Methods to Improve Reasoning Models
Read OriginalThis article details recent research advancements in improving LLM reasoning, focusing specifically on inference-time compute scaling techniques. It covers methods like Test-Time Preference Optimization, Chain-of-Associated-Thoughts, and Step Back to Leap Forward, explaining how increasing computational power during inference can boost performance on complex tasks like coding and math problems without altering the base model's training.
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