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BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 50 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 117 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 32 -
Scaling Laws for Downstream Task Performance of Large Language Models
Paper • 2402.04177 • Published • 20
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Grandmaster-Level Chess Without Search
Paper • 2402.04494 • Published • 70 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 32 -
Self-Play Preference Optimization for Language Model Alignment
Paper • 2405.00675 • Published • 29 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 62
-
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 50 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 117 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 32 -
Scaling Laws for Downstream Task Performance of Large Language Models
Paper • 2402.04177 • Published • 20
-
Grandmaster-Level Chess Without Search
Paper • 2402.04494 • Published • 70 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 32 -
Self-Play Preference Optimization for Language Model Alignment
Paper • 2405.00675 • Published • 29 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 62