Daily Papers
Daily paper digests generated from the Hugging Face papers feed.
- 2026-03-28 Paper: Rejection Sampling与Best-of-N在对齐中的应用
- 2026-03-27 Paper: UltraFeedback: Boosting Language Models with High-quality Feedback
- 2026-03-27 Daily Papers - 2026-03-27
- 2026-03-26 Paper: Zephyr: Direct Distillation of LM Alignment
- 2026-03-25 Paper: Orca: Progressive Learning from Complex Explanation Traces
- 2026-03-24 Paper: WizardLM: Empowering LLMs to Follow Complex Instructions (Evol-Instruct)
- 2026-03-23 Paper: SPIN: Self-Play Fine-Tuning
- 2026-03-22 Paper: Proximal Policy Optimization Algorithms (PPO)
- 2026-03-21 Paper: KTO: Model Alignment as Prospect Theoretic Optimization
- 2026-03-20 Paper: ORPO: Monolithic Preference Optimization without Reference Model
- 2026-03-19 Paper: Direct Preference Optimization (DPO)
- 2026-03-18 Paper: Constitutional AI: Harmlessness from AI Feedback
- 2026-03-17 Paper: LIMA: Less Is More for Alignment
- 2026-03-16 Paper: Stanford Alpaca: An Instruction-following LLaMA Model
- 2026-03-15 Paper: Self-Instruct: Aligning Language Models with Self-Generated Instructions
- 2026-03-14 Paper: Training language models to follow instructions with human feedback
- 2026-03-13 Paper: Scaling Laws for Neural Language Models
- 2026-03-12 Paper: RoFormer: Enhanced Transformer with Rotary Position Embedding
- 2026-03-11 Paper: GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
- 2026-03-10 Paper: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- 2026-03-09 Paper: PaLM: Scaling Language Modeling with Pathways
- 2026-03-08 Paper: DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
- 2026-03-07 Paper: Textbooks Are All You Need
- 2026-03-06 Paper: RWKV: Reinventing RNNs for the Transformer Era
- 2026-03-05 Paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces
- 2026-03-04 Paper: Mistral 7B
- 2026-03-03 Paper: LLaMA: Open and Efficient Foundation Language Models
- 2026-03-02 Paper: Training Compute-Optimal Large Language Models
- 2026-03-01 Paper: Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
- 2026-02-28 Paper: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- 2026-02-27 Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- 2026-02-26 Paper: Language Models are Few-Shot Learners
- 2026-02-25 Paper: Language Models are Unsupervised Multitask Learners
- 2026-02-24 Paper: Improving Language Understanding by Generative Pre-Training
- 2026-02-23 Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 2026-02-23 Paper: Attention Is All You Need