06 — Papers I have read
Papers I have read and found valuable while learning about transformers, alignment, reinforcement learning, tool use, and autonomous agents. The original researchers and publication links are credited below.
Ashish Vaswani, Noam Shazeer, Niki Parmar et al.
Introduced the Transformer: an attention-based architecture that removed recurrence and became the foundation of modern large language models.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
Established deep bidirectional language pre-training and showed how a single pretrained model could be adapted to a wide range of language-understanding tasks.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver et al.
Combined deep neural networks with reinforcement learning in the DQN agent, learning successful control policies directly from high-dimensional visual input.
Long Ouyang, Jeff Wu, Xu Jiang et al.
Demonstrated a practical reinforcement-learning-from-human-feedback pipeline for making language models more helpful, truthful, and aligned with user intent.
Shunyu Yao, Jeffrey Zhao, Dian Yu et al.
Interleaves reasoning traces with actions, giving language-model agents a practical way to plan, use external tools, gather evidence, and update their decisions.
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì et al.
Shows how a language model can learn when and how to call external APIs with limited supervision, a central capability for reliable tool-using AI systems.
Noah Shinn, Federico Cassano, Ashwin Gopinath et al.
Introduces verbal feedback and episodic memory as a way for agents to reflect on failed attempts and improve later decisions without updating model weights.
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai et al.
Presents an agent architecture built around observation, memory, reflection, and planning to produce believable long-horizon behavior in an interactive environment.