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Self-Rewarding PPO: Improving LLM Generalization from Demonstrations

Unveiling Hidden Data: How Alignment Information Leaks from Open Language Models

Upgrading Multimodal AI Data: The VERITAS Pipeline

Boosting Open Multimodal AI: A New Data-Centric Approach with Honey-Data-15M and Bee-8B

Learning to Reason: Why Patterns Outperform Sheer Rationale Quantity in LLMs

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Unlocking Generalization in Supervised Fine-Tuning

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Unveiling the Hidden Costs of Fine-Tuning: How Too Much Data Can Harm LLM Knowledge

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Making LLMs Safer and More Helpful: Insights from Fine-Tuning OPT-350M

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Advancing LLM Training: A Deep Dive into Reinforcement Learning and the GRAPE Framework

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PLaMo 2: Hybrid Architecture and Synthetic Data Drive Japanese LLM Performance

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CoreWeave Strengthens AI Capabilities with Acquisition of OpenPipe, a Seattle-Area Reinforcement Learning Innovator

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Proximal Supervised Fine-Tuning: Stabilizing LLM Updates for Broader Capabilities

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Simplifying Synthetic Data Creation for Advanced Language Models with GraSP

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Optimizing Data Mixtures for Fine-Tuning Large Language Models

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Efficient LLM Finetuning: Prioritizing Tasks for Better Results

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Boosting Mathematical Reasoning in LLMs: A Two-Stage Training Strategy for Accuracy and Efficiency

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Gen AI News and Updates

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Self-Rewarding PPO: Improving LLM Generalization from Demonstrations

October 27, 2025

Unveiling Hidden Data: How Alignment Information Leaks from Open Language Models

October 22, 2025

Upgrading Multimodal AI Data: The VERITAS Pipeline

October 20, 2025