Alec Radford
6 papers · Latest:
GPT-4 Technical Report
GPT-4 is a large-scale multimodal Transformer model achieving human-level performance on professional and academic benchmarks through advanced training and alignment techniques.
Evaluating Large Language Models Trained on Code
Codex, a GPT model fine-tuned on GitHub code, significantly outperforms prior models in generating correct Python programs from docstrings, demonstrating strong code synthesis capabilities.
Learning Transferable Visual Models From Natural Language Supervision
This paper presents CLIP, a model that learns versatile visual representations by training on 400 million image-text pairs, enabling zero-shot transfer to diverse vision tasks without task-specific training.
Language Models are Few-Shot Learners
GPT-3, a 175 billion parameter language model, demonstrates strong few-shot learning abilities across diverse NLP tasks without task-specific fine-tuning.
Scaling Laws for Neural Language Models
This paper identifies power-law scaling relationships between language model performance and factors like model size, dataset size, and compute, enabling optimal training strategies under fixed compute budgets.
Proximal Policy Optimization Algorithms
Proximal Policy Optimization (PPO) introduces a simpler, more efficient policy gradient method that improves sample complexity and performance across various reinforcement learning tasks.
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