How Klear-Reasoner Achieves SOTA Math & Code Reasoning with GPPO

Klear-Reasoner, built on Qwen3‑8B‑Base, introduces the Gradient‑Preserving Clipping Policy Optimization (GPPO) algorithm to overcome traditional clip limitations, achieving state‑of‑the‑art performance on AIME2024/2025 and LiveCodeBench while providing detailed experimental analysis and data‑quality insights.

GPPOLarge Language ModelsReinforcement Learning

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How Klear-Reasoner Achieves SOTA Math & Code Reasoning with GPPO

DataFunSummit

Feb 14, 2023 · Artificial Intelligence

Deep Learning Hyperparameter Tuning and Training Tips: Insights from Zhihu Experts

This article compiles practical deep learning training and hyperparameter tuning advice from Zhihu contributors, covering model debugging, learning‑rate strategies, optimizer choices, data preprocessing, regularization techniques, initialization methods, common pitfalls, recommended research papers, and ensemble approaches.

Deep LearningRegularizationgradient clipping

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Deep Learning Hyperparameter Tuning and Training Tips: Insights from Zhihu Experts